Inventive Communication and Computational Technologies

Exchanging data over the system has generally utilized quick and solid hot spot for correspondence. Clients from wide devotion utilize this component for exchanging and retrieving the required data. Portability and operability inside cloud framework are achieved through disconnected and online mediums, which are persistently alluring, yet the issue of security emerges amid the transmission process. Security and unwavering quality are the key issues during the exchange process, which requires serious research consideration. Data security is achieved by utilizing the public and private key-enabled block-level DNA-based EX-3 code. The analysis is inferred on the disconnected information as well as on the online information, for example, Google Docs. Redundancy handling mechanism is utilized to guarantee space for the information storage supplier, which remains a minimum utilized characteristic as it considers the capacity utilization in DSP. With the proposed system, the overall space allocation for heavy documents is decreased and online data security has been improved by the utilization of byte-level DNA-based EX-3 code.

Cloud computing permits plentiful access to shared pools of resources that are configurable and provide higher-level services to the user, which can be easily and hastily granted with minimal management effort. With the advancements in cloud computing, many cloud service providers have started using the distributed high-end servers to provide services to its users. However, while designing an efficient cloud environment, it has been found that the application portability is a key issue. Portability is usually offered to mitigate supplier lock-in. Nevertheless, shifting from a single method to an alternative for a minimum of work, seeing that is quite possible together with box companies, which can also improve the durability along with scalability. Therefore, in this paper, a novel TensorFlow-based semantic technique is designed and implemented to significantly port the applications between high-end servers. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. Extensive experiments reveal that the proposed technique outperforms the existing techniques.

NoSQL allows fast processing of real-time large data applications. It allows database professionals to make use of elastic scalability of database servers. Most of these databases are created to perform on full throttle despite the presence of low-cost hardware. Users are able to create the database more quickly due to the non-relational nature of NoSQL. There is no need of developing the detailed (fine-grained) database model. As a result, it will save lots of development time. The primary aim of this paper is to present a novel data integration method as well as workflow that executes the data integration of various source systems in such a way that user does not require any programming abilities. Middleware is considered as main part of the proposed technique which provides high availability of data in case of failures by utilizing the checkpointing. Experimental results reveal that the proposed technique provides efficient results, especially in case of any kind of failures in distributed NoSQL servers.

Automatic bottle filling using programmable logic controller (PLC) constitutes a user-specified volume selection, in which the user can input the desired amount of liquid or water to be inserted in the bottles. It is generally used where many bottles of same volume are to be filled by passing bottles over the conveyor belt. PLC is a main functional block in the automation which tries to minimize the complexity and increases safety and cost reduction. Using of PLC in filling the bottles allows us to select the required amount of liquid by the ladder language. Filling is done by using motor, sensors, conveyor belt, PLC, solenoid valve, and so on. The whole system is more flexible and time saving. The process of filling is carried out for packaging of liquid and beverages. This is an interdisciplinary branch of engineering which includes mechanical, computer, and electronics parts. The process also enhances the knowledge of fabrication, programming, design, planning, and presentation skills. The PLC is gaining popularity because it is easy for troubleshooting which makes programming easier and reduces downtime.

At present, with the expansion of size of the internet, security plays a crucial role in computer networks. Also with the advancement of Internet of things, earlier technology like firewall, authentication and encryption are not effective in ensuring the complete security. This has lead to the development of Intrusion Detection Systems (IDS) which monitors the events in computer networks to recognize the threats that violates computer security. With the help of various machine learning algorithms we have carried out the implementation of IDS. Machine learning technique increases the accuracy of anomaly detection in real-time scenario. This work focuses on K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM), which classify the program behavior as intrusive or not. To prevent DoS (Denial-of-Service) attacks, a new method is implemented in this paper. The KNN classified data which provides malicious IP address are blocked in routers through Standard Access-list.

The design of Silica Glass photonic crystal fiber is proposed for lowering the chromatic dispersion. For this design, Finite Difference Time Domain (FDTD) methodology along with the transparent boundary condition (TBC) is applied. This method produces zero dispersion at 0.5 to 1.5 μm diameter of circular and elliptical air holes. These types of PCFs have high potential as like the dispersion compensating fiber (DCF) in optical window. The refractive index is calculated with Sellmeier equation in this method which is equal to the conventional silica glass, i.e., 1.457. The proposed design is also used to show the non-linear effects of the material used.

In recent times, wireless network builds a bridge between the physical and virtual worlds. It poses security threats at different layers. The most vital security issue is observed on the network layer on different wireless networked systems. Defense mechanisms are used to shield the network layer from these repetitive malicious attacks. Malicious hubs are detected and differentiated along the network by using these mechanisms. These networks truly require a unified security answer for ensuring both course and information transmission tasks on the network layer. Without a suitable security solution, malicious nodes in the network can disturb rapidly across the network. This just irritates the system activity from exact delivery of packets and malevolent hubs that can disturb transmissions or drop all packets going through them. The main objective is to outline the different attacks and the countermeasures used to ensure the network security against pernicious attacks.

“Internet of Things” is now a subject that is increasingly growing on both the job and modern devices. It is a concept that maybe not just get the potential to influence how we live but in addition how we work. Intelligent systems in IoT machines in many cases are used by various events; consequently, simultaneous information collection and processing are often anticipated. Such a characteristic that is exclusive of systems has imposed brand new challenges towards the designs of efficient data collection processes. This article is to be discussing various layers in Internet of things. Those layers are sensing layer, network layer, service layer and application layer. Various data processing techniques are integrated along with data filtering and data conversion. Protocol transformation is also feeling the major challenges faced by enterprises wanting to shift to the style in brand new technology.

Performance of routing protocols used for multicasting is highly depending on its route searching approach, which is the first step of routing process. Expanding ring search (ERS) is one of the widely used concepts for route searching process which is also used to reduce the broadcast overhead in MANET. To understand the effect of route discovery process on the performance of routing protocols, we have considered two different multicast routing protocols, namely energy-efficient ERS (E2ERS) and Multicast Ad hoc On-demand Distance Vector Protocol (MAODV) which are widely used for multicasting in MANET. MAODV uses ERS approach whereas E2ERS has modified the ERS approach by modifying the transmissions of RREQ during route discovery process. Here, we have used different parameters like end-to-end delay, goodput and packet delivery fraction for varying node speed and varied TTL_Increment values for varied number of receivers to compare the performance of MAODV and E2ERS protocols.

The raising of Cyberwarfare level require to have the maximum protection levels in the networks and its applications. Therefore, these systems must be protected from unauthorized usage of their data using the most powerful security algorithms. Many and growing challenges facing cybersecurity require cryptographers and data security engineers to develop stronger and more powerful algorithms to resist this type of attack. There are many characteristics that measure the strength of the electronic security system, including the speed of the algorithm and the strength of the system against the cryptanalysis. This paper discusses the design, building, and implementation of a new hybrid algorithm that combines a number of security algorithms in order to obtain the best results in terms of resistance to multiple cyber-attacks. This paper also contains an analytical study of the algorithms used and their effectiveness in resistance to attacks represented by statistical results and graphs.

This paper discusses the implementation of a novel technique of encoding data bits using the concept of bit stuffing in addition to the conventional methods of source coding. This technique can be applied to any of the existing methods of source encoding under controlled conditions. In particular, the method is very efficient when the encoded bits have more number of ones or zeros than a predefined threshold, at any point of time and in any part of the stream. Usually, bit stuffing is a common method used for data compression in data communication layers to reduce the bandwidth. In this paper, we have attempted to incorporate bit stuffing in various encoding schemes and have compared the improvement in performance with and without bit stuffing. The software used for simulation is MATLAB. The primary motivation of this work is to determine the maximum amount of bandwidth savings that can be achieved due to bit stuffing for a random series of alphabets.

Network on Chip is a special unique case of parallel computing systems defined by the tight constraints such as availability of resources, area, cost of the NoC architecture and power consumption. NoC is designed with three main components: switches, Network Interfaces (NIs) and links. NoC is used for several application domains, such as multi-media processing, consumer electronics, biological applications, etc. NoC is the technology proposed to solve the shortcoming of buses. This technique is used to design communication subsystem among IP cores (Intellectual property core) in a SoC design. In this chapter, we have discussed about 3D integrated circuits, 3D wired and wireless NoC, Emerging Technologies, and Literature Survey.

With the substantial increase in the internet usage and the growing threat of hackers to infect as many devices as possible, security has become important to prevent data breaches and industrial sabotage. Unified Threat Management are the Next-Generation network security appliances that include multiple security features along with the performance required for future networks. But packet processing usually consumes 70% of the CPU time and during heavy load it degrades the UTM performance by dropping important packets. To overcome such limitations, many techniques and algorithms have been proposed by the researchers. In this paper, survey and numerical analysis of each technique is done based on the overall packet processing time. Based on the numerical analysis, we suggest the best technique to reduce the overall packet processing time in UTM and hence reduce the load under heavy traffic conditions.

The pomegranate fruit has gained popularity due to its nutritional values and pharmacological properties. India ranks high among growers of pomegranates across the world and hence there is tremendous potential for its export. Here in our work the Bhagwa, a prime Indian pomegranate cultivar was studied. Total soluble solids (TSS) were measured experimentally. Internal images of the fruit were obtained nondestructively using Magnetic Resonance Imaging (MRI). The textural features from image were given as input to the nonlinear autoregressive neural network. The results showed that T1-weighted MR images were sensitive to physical and chemical changes. The R-value for measured TSS and model-predicted TSS for training data was 0.99 and testing data was 0.92. This study shows that MRI has higher potential for nondestructive method of grading pomegranate fruit based on the chemical values which are the basis for determining the maturity of the fruit.

Differential privacy is a method adopted to check for any privacy breach that occurs during communication for the exchange of confidential information. Here, in this work, differential privacy is being implemented in the context of vehicular ad hoc networks (VANETs). In this paper, we implement the concept of differential privacy using the Diffie–Hellman key exchange algorithm and the advanced encryption standards (AES) algorithms that are very powerful in terms of their performance. Algorithms like Laplace and Gaussian algorithms, which are currently the most commonly implemented algorithms, have been used for verification. The algorithms were analyzed by considering a situation where an initial location and final location have been defined and these have been encrypted using the mentioned algorithms and the privacy has been preserved.

The most intelligent creatures on the earth are homosapiens since their brain is gifted with the ability of thinking and expressing emotions through different means. The human brain is a universe consisting of a cluster of neurons connected to each other. There are about 100 billion neurons in the human brain. Estimated that a neuron connection transmits at least one signal per second, and some theories proved that some of the specialized connections transmit up-to 10,000 signals per second [1]. Briefly, we can say that when we experience something through sense organs which are having direct contact with our brain, release some or other chemical called hormones. For instance, if we feel something ‘very good to our heart!’ or like start loving something by sensing anything, our brain produces a hormone called ‘oxytocin’. As there is an extraction of this hormone happens in a ‘Patterned’ manner from an import gland of our human body called as the pituitary gland, the pattern creates an electrical signal/impulse which transmitted to our brain in a fraction of seconds, making us experience the emotion called love on that particular object or person. The electrical impulse created here and transmitted to the brain is nothing but a thought/emotion signal. Our main aim is to extract a converted written text or an image of the electrical impulse created, this electrical impulse is extracted out from a human brain through EEG signals.

Every communicationPradeep, R. systemSunitha, N. R. is aRavi, V. safety-criticalVerma, Sushma system, in which the communicating entities share the confidential data over the untrusted public network by using a set of cryptographic security protocols (CSPs). Many security protocols proved secure were cracked within a short span of time, and the best example is Needham–Schroeder authentication protocol. The quality assurance about the correctness of security protocols is one of the key challenges. In software testing, it is not possible to prove the correctness of security protocols, because testing has got major drawbacks and the tester cannot predict what knowledge an attacker may gain about the communication system by interacting with several runs of the protocol, and also testing shows the presence of bugs but can never show the absence of bugs. Formal verification has proved to be a reliable solution as the correctness of the CSP can be proved mathematically. In the proposed work, a new framework is proposed, which includes a library of functions to specify a security protocol in C++ by following a set of rules (syntax and semantics), a interpreter to interpret the C++ code to security protocol description language (SPDL), and finally a model checker Scyther backend verification engine. The proposed framework is successful in identifying the attacks on IKE version-1. Also the Skeme and Oakley versions were verified for their correctness.

The cutting edge scheduler of containerized cloud administrations considers load balance as the main rule, numerous other imperative properties, including application execution, are ignored. In the period of Big Data, applications advance to be progressively more information escalated and subsequently performed inadequately when conveyed on containerized cloud administrations. With that in mind, this paper means to enhance the present cloud administration by considering application execution for the cutting edge compartments. The more explicitly, in this work we fabricate and break down another model that regards both burden equalization and application execution. Dissimilar to earlier examinations, our model edited compositions the predicament between burden equalization and application execution into brought together steaming issue and after that utilizes a factual technique to effectively settle it. The most difficult part is that some sub-issues are amazingly unpredictable (for instance, NP-hard) and heuristic calculations must be formulated. To wrap things up, we actualize a framework model of the proposed planning procedure for containerized cloud administrations. Exploratory outcomes demonstrate that our framework can fundamentally support application execution white safeguarding generally high burden balance.

The dark web has turned into a dominant source of illegal activities. With several volunteered networks, it is becoming more difficult to track down these services. Open source intelligence (OSINT) is a technique used to gather intelligence on targets by harvesting publicly available data. Performing OSINT on the Tor network makes it a challenge for both researchers and developers because of the complexity and anonymity of the network. This paper presents a tool which shows OSINT in the dark web. With the use of this tool, researchers and Law Enforcement Agencies can automate their task of crawling and identifying different services in the Tor network. This tool has several features which can help extract different intelligence.

The Big data and cloud integration is a challenging Task. To enhance the data security issues, ABE can be deployed. In proposed model, a improved concept has been implemented and the integration of cloud and Big data is achieved. Security is the major threat for cloud computing applications. Every user has to feed user name, password, and primary key for Data access into the cloud data center. Data owner generates a new key to the users for accessing the data. Policy updating is also implemented in the proposed system, that is the accountability for the data access has also been implemented. In case of the change of policy, the altered data stored in the cloud is not affected. In addition to that, admin generates policy key based on the user’s profile. If any user tries to misbehave, an immediate alert is sent to the data owner. Data owner can change the policy key and access policy in the run time. Our system should be able to update its policy automatically.

The key emphasis on Control Loop Performance Monitoring (CLPM) includes the detection of oscillations in control systems. Oscillations are the results of plant performance degradation and are a very common problem that occurs in the control loops of the process. This paper discusses the technique for detecting oscillations in process variables. The occurrence of oscillation in control loops, results in deviation from the setpoint, hence reducing the productivity and thus the profitability. Oscillations in control loops may be due to several causes such as aggressive tuning of the controller, external disturbances and sometimes may be due to stiction in control valve. Low maintenance of valves frequently produces large oscillations in a process which in turn affects the throughput. In time domain, detection of oscillations is tough when the signal includes disturbances. In frequency domain, Bispectrum analysis is a great tool for the detection and analysis of oscillations. To detect the oscillatory behaviors, the Modified Bispectrum tool was applied to a highly nonlinear Shell and Tube Heat Exchanger (STHX) process.

This paper mainly focuses on implementing a new approach rather than using the conventional method of generating an intermediate frequency in a mixer. The ordinary mixer is compared with a switching mixer through several techniques in order to strengthen the point that switching mixers have a better performance. Theoretically, we know that switching mixers are more efficient but here we also give it a practical justification through this paper. Software such as MATLAB and Proteus has been used for the same. The main objective of this research work is to determine the maximum amount of noise that can be removed to obtain a good reconstruction of the input signal.

We propose three innovative location and mobility-independent computationally-light metrics to quantify the stability of links in mobile sensor networks (MSNs). The proposed metrics (Normalized Neighbor Degree: NND, One Hop Two Hop Neighbors: OTH, and Fraction of Shared and Unshared Neighbors: FSU) are computed on the egocentric network of an edge and the hypothesis is that larger the extent of shared neighborhood between the end vertices of an edge, larger the stability (lifetime) of the link in the MSN. The computation times of all the three metrics are about 15–40 times lower than the computation times of the bipartivity index (BPI) and algebraic connectivity (ALGC) metrics that were adapted from Network Science in an earlier research to quantify link stability in MSNs. The lifetimes of the DG trees obtained with the proposed computationally-light link stability metrics are appreciably larger or comparable to that of the ALGC and BPI-based DG trees.

Mock is anSoni, Anshu object thatRanga, Virender replicatesJadhav, Sandeep the behavior of a real object in a disciplined way and improves unit testing. Unit testing is a testing where each individual or component is tested. The purpose of unit testing is to validate each unit of designed software and allow to verify the generated code is working properly, regardless of its dependencies. A system under test has some external dependencies like APIs and creating a mock object based on that kind of dependencies would be efficient rather than generate a test case on the actual instance of the dependencies. A real working system such as banking, autonomous vehicles, online-supply chain businesses, and E-commerce platforms are heavily dependent on a server and facing difficulty while testing with a real server. Mock server helps in testing by simulating the behavior of a real server. Mocks could be used for testing and developing the front-end even when the back-end is not available. The aim of our research work is to propose a generic approach in which we propose a mock framework named MockRest for REST API in Java. The main reason to propose such kind of framework is to get a consistent response while real API is down at the moment by creating a mock of REST API as it allows the developer to stay constructive while the API is being implemented. Application Programming Interface (API) allows interaction between software programs, exchanges their information while REST is an architectural style, and applies to the design of API. A Web API that follows the standards of REST architectural style is a REST API. Based on the description of Web services by its interface, Mock simulates its behavior.

DDS is a real-time protocolParmar, Navrattan forRanga, Virender fastSimhachalam Naidu, B. communication. It implements Data-Centric Publish–Subscribe (DCPS) implementation and an optional higher-layer Data Local Reconstruction Layer (DLRL). DCPS ensures the reliability of message delivery to proper recipient and uses in syntactic interoperability in different platforms and languages. ROS is a widely used platform to develop robots, drones, and other cyber–physical systems (CPSs). ROS 2 is built on top of middleware DDS and provided abstraction in communication. Adaptive AUTOSAR (Automotive Open System Architecture) also adopted the DDS standards as one of the communication bindings. This research paper proposes connection establishment and interoperability between ROS 2 and Adaptive AUTOSAR software using DDS as a middleware. Interoperability is the major challenge with the increasing number of IoT devices, being solved by DDS. The outcome of this research is useful for autonomous cars, and the proposed concept can be extended for fog computing and other interoperability problems. DDS will bring a revolution in the near future in automotive industry, smart grid, smart homes, and other smart applications.

In case of water quality monitoring, smart solutions are gaining more importance with communication technology. By using a specific equipment, the quality of water is tested on each attributes like minerals, temperature and so on. This process consumes more time to complete for the given sample. This paper contains two main activities. In application side, a detailed survey on recent work has been carried out to perform smart water quality monitoring in terms of application, communication technology used, and types of sensors employed. The next is by using controller with inbuilt Internet connectivity module to monitor parameters such as temperature and turbidity using low cost and less complex smart water quality monitoring system. The system contains an appropriate webpage for enhancing the user convenience on the deviation of water quality parameters.

Along with number of problems associated with WSNs, one of the major issue we chose to study is their clustering topology and energy utilization techniques. Both these parameters are very much responsible in determining the life of nodes, quality of service, delay in data transmission, etc. So, it is very important to examine the cluster-based routing protocols and energy optimization protocols. The cluster routing is based on the selection of cluster head nodes for data transmission, and the parameters which are used for the making of an optimized network mainly depend on number of nodes, position of base station, and the network size. So, a well-settled cluster is tried to be designed with the study of various algorithms early proposed. And for energy optimization of network, we studied “sleep/wake-up” algorithm, which makes the nodes sleep during its ideal mode and wake it up when data transmission is to be done. With this sleep/wake-up algorithm important point to be kept an eye is the delay during this shifting between sleep and wake up or making sure that the selected path should not be engaged as well as it must be shortest path from base station. So, based on these two protocols, various papers are studied, and the comparative result of this protocol in various scenarios is carried out.

Development of mobile communication is rapid, with different methods and techniques being introduced in wireless communications. The next few pages will deal with the detailed study of wireless cellular technologies—first, second, third, and fourth generations eventually leading to the fifth generation as well. This will visualize the evolution from analog system transmissions to digital transmissions which brought the usage of audio, graphics, video, etc. The evolution also gave rise to Internet on the cellular mobile phones which were once realizable only in computers through broadband connections. With improved technologies, we saw the development of fourth-generation cell phones which harnessed the use of LTE. The world is moving very fast, and the corporates involved in mobile communications are in a strong tussle to achieve the fifth generation of mobile networks which will shift the world’s entire way of functioning from autonomous vehicles to IoT and many other things which could be seen only as part of sci-fi movies.

Most of the times data for certain task seems to be varying due constant changes made to method of data collection as well as due to inclusion of new parameters related to the task. This may result in false conclusion derived from data generated and might lead to failure in task or degradation in the standard of activity related to that task which is being monitored from that data. Clustering is basically the grouping of similar kind of data wherein each cluster consist of data with some similarities. Whereas most of the data is unstructured or semi-structured, and that’s where unsupervised K-means Clustering method plays role to convert the data into structured one’s for clustering. This paper consist of K-means clustering method which is being used to keep an eye on such variations which are occurring in data generated for a task when certain changes are incorporated in technique to track this data.

Mobile cloud computing (MCC) is the convergence of two recent technologies namely “Cloud Computing” and “Mobile Computing” with wireless networks as a communication backbone. There are mainly three paradigms that use the concepts of MCC, viz. edge computing, fog computing and cloudlets. Due to the presence of various heterogeneous hardware and software platforms in MCC, there are many interoperability issues which create vendor/services lock-in problems, it also makes data and application portability difficult. This paper studies the different paradigms of MCC and the challenges in making them interoperable in heterogeneous hardware and software platforms. We have summarized some of the MCC-based research papers and their findings. Contribution of this paper is the summary of challenges and research scopes in the field of MCC where it needs to be addressed to mitigate the interoperability issues.

Advancement in the field of wireless communication technologies emerges as a research area of increased interest due to the recent developments in 5G and Internet of things. Newer and better methodologies of modulation will help to achieve efficiency in energy and judicious use of spectrum. Filter bank multi-carrier modulation scheme in the context of 5G is a better alternative to any of the currently deployed schemes. This promises better intersymbol interference and intercarrier -interference factors, and hence, it is ideal for massive MIMO scenarios. A comparative study of FBMC system with the presently deployed OFDM scheme is also analysed.

Nowadays, it is important to automate the processes in farming. For efficient farming, we can use embedded systems and IoT by which farming could become like a video game. Robots in control are more than a simple case study. We are demonstrating a simple example of Ploughing in which Tractor has its own vision, through which it can identify the boundary of the field and can plough the field without any driver. A farmer can easily operate various farming operations on his smartphone in just one tap. The vision is able to detect the poles on the boundary of farm, and using image processing, camera on tractor is able to detect the colour flag on a pole, and by detecting the colour of the pole, it is able to turn by its own and plough the field completely and stop. We can use this concept to automate the seeding, irrigation, weeding, harvesting, delivery, etc., depending upon the crop.

The aim of this work is to design and develop efficient and low-cost end effector for holding and handling of fragile objects, which is to be used in the industrial automation and mainly for the purpose of domestic robots. The design consists of three systems: the sensor system, the control module, and the robotic end effector. Different objects have different stress-handling capabilities, when pressure is applied on the object goes beyond the yield point, and then object either deforms or breaks. So, the aim of this work is to utilize that strain being applied to the object for holding the object and handling it carefully. This end effector works with a single degree of freedom. The end effector is designed to have three grasping fingers out of which one is stable with the sensor on it and other two fingers are attached to a single joint with common motion to both fingers.

Images captured using smartphones and video cameras are recorded and can be used anywhere and at any time. While taking a quick shot or while capturing the moving objects, it may lead to the motion blurred images. In order to recover the sharp images from motion blurred images, blind motion deblurring can be used. Motion deblurring can be done by knowing both edge and non-edge of motion blurred images. Edge and non-edge are the two methods used in total variation and alternate direction method for deblurring of digital images. Step edges can be predicted and detected by using edge-specific method. In non-edge method, it explores various image statistics, such as the prior distributions and it is sensitive to statistical variation over different images. Both methods are used in large dataset images, but it fails extremely in simple images. To overcome this problem, total variation (TV) based regularization method is used which is followed by an iteratively reweighted algorithm based on alternating direction method. To get higher results, LSED prediction—based technique is employed, which first of all restores sharp edges and then uses them to estimate initial kernel that traps the optimization of local minimum corresponding to sharp images.

A compact multiple-input-multiple-output (MIMO) antenna for wireless local area network (WLAN) applications with dual-band characteristics is presented. The proposed antenna is composed of two microstrip line-fed fractal triangular microstrip antenna. To achieve good return and high isolation, a Z-shaped stub is added in the ground plane. To reduce the mutual coupling and ECC, a Z-shaped stub is added to the ground plane. The proposed antenna covers the bandwidths of WLAN 5.2 GHz (5.11–5.23 GHz) and 5.8 GHz (5.72–5.92 GHz). A high isolation over −25 dB is achieved for both the IEEE 802.11a bands. The MIMO antenna return loss, envelope correlation coefficient, gain and radiation characteristics are also investigated. The results indicate that the MIMO antenna is suitable for WLAN applications. The geometry of the proposed dual-band WLAN MIMO antenna has an overall size of 68 × 26 × 1.6 mm3.

In this paper, we present the theoretical analysis of Bragg waveguide containing a random multilayer defect. Transmittance spectrum of the proposed multilayered cylindrical waveguide is obtained by employing transfer-matrix method. Presence of random defect structure shows multiple defect peaks in the photonic band gap of a defect-free Bragg structure. The probability of occurrence of defect peaks around 660–690 nm is found to be very good, and these peaks may be chosen as a sensing element. Result shows that the transmittance of the defect peak has a good agreement with the change in the refractive index of the core.

Cloud computing is the recent technology used to share and store the computer resources rather than having resources in local server to maintain the application. Though cloud is used for a large amount of storage, there is no security in cloud. In general, all the groups have data owners and data members each should have user name, key, and group key. If a user shifts from one group to another, they can easily access the information from another group. It leads to a security problem. In order to increase security and confidentiality, author generates the new group key via Email using Diffie-Hellman algorithm. In case of a new user is added or an existing user leaves themselves from the group. The data members have to get permission from the data owners in case of any data updation. If the user misbehaves, i.e., (DDOS) attack, data owner or cloud terminates the user from the group. The updated key is sent to the users through Email. This mechanism significantly improves security in cloud computing.

Today’s era is of cutting edge of innovations as well as technologies. One of the major problems, researchers often face is an issue looking for an appropriate research area. For instance, there are numerous fields these days on which research is being carried out and to pick one out of those topics is itself a challenging task. The major objective of this review paper is to embark upon Artificial Intelligence (AI) that prompted the emergence of deep learning (DL) and further to convolution neural networks (CNNs). Limitations of CNNs that led to the development of Capsule Neural Networks (CapsNets) have been included. The significant goal of this review paper is to discuss the latest trends in which research is on-going and is still in progress. Also, the key challenges faced by past researchers are highlighted.

In simple words, data is the real specifics associated with any objective for consideration. Data, in the perspective of databases, corresponds to all particular items that are stored in a database, moreover separately or as a set. Data in a database in general is preserved in database tables, which are ordained into columns with the intention to enforce the data types retained therein. Database is a staged collection of data and elaborates them as information; more exclusively, a database depicts an electronic framework which eases data to be modestly accessed, manipulated and updated. In other words, whenever an organisation having database it embodies method of storing, managing and retrieving information and they are governed by database management system (DBMS) to make it flexible. Generally, databases encompass multiple tables with numerous different fields suitable to the data stored in the table. Database management system (DBMS) let its users to retrieve information in the database and operate data. In addition, it controls the access to the database. This paper deals with Indian GST rates for various categorical products into the Derby DB using NetBeans as IDE and Glassfish application server. This system can be manipulated and enhanced with the simple SQL queries. This system proves to be a smart way for creating a secure, standardised and transactional Derby DB.

The network services are reducing the secure communication through preventive detection of intrusion services. Besides, different types of network transmission host the intrusion detection services by data centres that vary of server organisation mechanism. Network security topology is afflicted of one application service by a different communication reciprocal that assume poor performance through application services. Further, work factors that can affect the communication packets performance by including network infrastructure component failure, configuration issues, or damage cost of informal distinct components. The intrusion detection services are the community around time for servicing a request of main user. This work has been proposed to Whole of Service Anomaly Detection (WoSAD) methods, which remain more effective than the alternative route transmission and to the whole of service models.

Mobile Ad hoc Network (MANET) is a self-sorted out remote system, comprising of independent nodes. The correspondence in the MANET is of multihop in nature because of non-attendance of any settled foundation or centralized base. An assailant, it may encroach effectively into MANET by acting like authentic middle of the road hub and present different kinds of security assaults on information exchange occurring among source and goal. In this paper, we have simulated the blackhole assault in AODV reactive routing protocol of MANET and investigated its viability by considering various performance metrics.

Cryptography is a scientific art which deals with the methods for changing over messages, data and information in some dynamically and haphazardly planned language of characters which are mixed up and not understandable for people or notwithstanding for a machine. While doing this, it is guaranteed that authorized frameworks and humans can recuperate the original message by validating their authentication. There are different calculations and strategies are proposed for cryptography purposes. This paper focuses on examination of two individual cryptographic calculation RSA and Blowfish, how they work and what will be the impact on security and speed if these two are converged into one hybrid calculation with appropriate modification. This paper presents the comparative analysis in terms of encryption and decryption time for individual as well as hybrid algorithm.

Strong encryption provides support to data privacy. Although encryption can make the data secure in data transfer and at rest, at some point this encrypted data definitely needs to be decrypted. Now at this very point, it so happens that the data becomes susceptible to attacks ultimately resulting in compromised data privacy. This is where secure multiparty computation (MPC) comes into picture; thereby, it provides ability to calculate required values from numerous encrypted data sources without any party compromising on their secret data. At ground level, MPC is a very general concept that can be realized using different protocols, such as secret sharing, in which secret data from each party is divided and then distributed randomly, encrypted “shares” among the parties. This distributed data when eventually aggregated would provide the final desired result. If anyone happens to intersect the data at hand of any of the parties, it would prove futile. With this paper, we focus on different algorithms or techniques that work behind the scenes in implementing MPC. These techniques include homomorphic encryption, followed by RSA combined with Paillier’s algorithm and lastly the concept of garbled circuits.

This paper presents the power and delay comparison of 7:3 compressor circuit designed using three different architectures of XOR gate which are based upon mirror circuit, 4-transistor, (4-T) and transmission gate (TG). The compressors have been implemented in transistor level at 180 nm technology and the functionality is verified in Cadence-spectre. Among the 7:3 compressors, the design utilizing the TG-based XOR gate is exhibiting least power consumption and the least delay is exhibited by the design which is based upon mirror circuit-based XOR gate.

There is a growing need in the industry toward the development of new and sophisticated tools for storing the exponentially growing volume, velocity and variety of data, which is collectively referred to as big data. There has been a paradigm shift from traditional data warehousing techniques to inclusion of NoSQL technology in order to fulfill the requirements of big data. While Hadoop has powerful features, which is not a replacement to Data Warehouse, rather it is a complement. Data Warehouse is already good at processing structured data so when used in conjunction with Hadoop, it becomes a winning combination. Hadoop can be considered as one of the back ends of Data Warehouse for handling unstructured data. Hence there is research on enhancing existing Data Warehouse with new features that have been successful at handling big data, and most popular one among them is MapReduce. We discuss the different tools and techniques used for improving Data Warehouse by adding these features and discuss the limitations associated with them.

This paper addressesHublikar, Shivaraj howEligar, Vijaya to detectKakhandki, Arun denial-of-service attacks using sFlow. Denial-of-service (DoS) attack is a critical security challenge in software-defined network (SDN). In DoS attack, the network bandwidth is acquired by disrupting the services of the server by abruptly increasing the traffic and making the server unavailable for other users. The most challenging problem of DoS attack is to detect the attack almost instantly and in a precise manner. This paper presents the detection of DoS attacks by using sFlow analyzer, a SDNs flow monitoring tool. In the event of any attack, sFlow collects sample packets from network traffic, analyzes suspicious behavior and creates handling rules which are then sent to the controller. Implementation of DoS attack is carried out by emulating a typical network in Mininet and integrating this with sFlow analyzer. Through the simulated results, the potential DoS victims and attackers are quickly found.

Digital FIR filtersEligar, Sanjay have beenBanakar, R. M. used for various signal processing tasks in embedded systems. This paper presents a novel architecture for implementation of an FIR differentiator in FPGA designed for a specific application. The designed component is to estimate the velocity from the suspension displacement in a semi-active suspension controller. The architectures of FIR are modified based on the direct and transposed form, with incremental changes in the manner in which the constant-coefficient multiplication is executed. The recoding of constant FIR filter coefficients using Canonic Signed Digit representation is explored. Three architectures are designed and compared for efficient usage of area of implementation in FPGA. It is observed that the architecture using CSD requires 16% lesser area, and the optimized architecture is an additional 5% more area-efficient and $$16\%$$ lesser in complexity of design because of sharing of resources. An overall reduction in power consumption by $$3\%$$ is observed and the computation of the FIR filter convolution sum is faster by $$17\%$$.

Breast malignancy is the second biggest disease that results in fatal condition for women population. Research endeavors have revealed with expanding affirmation that the support vector machines (SVMs) have more noteworthy precise conclusion capacity. In this paper, breast disease determination is dependent on a SVM-based technique that has been proposed. Investigations have been directed on various preparing test allotments of the Wisconsin breast malignancy dataset (WBCD), which is generally utilized among scientists who use machine learning strategies for breast disease conclusion. The working of the technique is assessed by utilizing characterization precision, particularity positive and negative prescient qualities, collector working trademark bends, and perplexity lattice. The outcomes demonstrate that the most elevated grouping precision (99%) is achieved for the SVM.

In this paper, the distinction between prototype and product is clearly described. During product design, there are many processes involved which pave a way for an operational product. The discussion on different types of prototypes gives the designer a better insight into the product deployment and testing phase. The in-depth knowledge of the testing involved in different types of prototypes assures that all the technical specifications and product specifications are considered. A questionnaire is developed to design the product that gives the detailed description of the design flow which is to be followed during the development phase. The field testing steps to be followed are known in advance if a proper design flow is adopted. The concept of “Engineering, Design and Process” is discussed in detail for the product design approach.

Microarray data analysisMohapatra, Shubhankar is oneSarkar, Moumita of theMohapatra, Anjali major areaBiswal, Bhawani Sankar of research in the field computational biology. Numerous techniques like clustering and biclustering are often applied to microarray data to extract meaningful outcomes which play key roles in practical healthcare affairs like disease identification, drug discovery, etc. But these techniques become obsolete when time as an another factor is considered for evaluation in such data. This problem motivates to use triclustering method on gene expression 3D microarray data. In this article, a new methodology based on coarse-grained parallel genetic approach is proposed to locate meaningful triclusters in gene expression data. The outcomes are quite impressive as they are more effective as compared to traditional state-of-the-art genetic approaches previously applied for triclustering of 3D GCT microarray data.

Real-time data with redundant and irrelevant features can degrade the performance of the classifier. Dataset with more number of features also increases the noise of the data and increases the time complexity of the learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the dimensions of the data. In existing feature selection methods, the resultant feature sets can lead to local optima in the space of feature subsets. In this paper, ensemble-based feature selection approach is proposed to reduce size of the dataset and to improve classification accuracy. Results show that the proposed ensemble approach enhances the classifier performance, with reduced number of features.

Cloud computing is revolutionizing many ecosystems by providing organization with computing resources that feature easy connectivity, deployment, automation, and scalability. In the recent past, the attractive features of cloud computing fuel the consolidation of cloud environment in the industry, which has been consequently motivating research on the related technologies by both the academia and industry. Regardless of its advantages, computing paradigm raises security concerns in transition phase, which have subjected several studies. Cloud computing tends to offer scalable on-demand services to the consumer with greater flexibility and lesser infrastructure investment. Since cloud services are delivered using classical network protocols and formats over the Internet, implicit vulnerabilities existing in these protocols as well as threats introduced by newer architectures have raised many security and privacy concerns. In this paper, we have focused on the data security issues found in the cloud computing. In addition to this, we discovered an appropriate solution and a private cloud domain.

Internet of Things environment has collection of communication system of various devices with few heterogeneous and homogeneous characteristics, where each device has unique identification address for activate action over network. The main purpose of gathering information is to process the communications and evolution. IOT applications face several challenges such as energy efficiency, reliable communication, latency awareness, etc. The most emerging requirement in IOT application is to implement an effective energy conscious communication protocols and clustering techniques. These techniques are reducing energy utilization of nodes and increase the network lifetime and scalability. A robust clustering technique is essential for self-organizing sensor networks. Proposed work on this paper is hybrid method of LEACH with firefly technique for optimal cluster head selection. The proposed hybridization methods used for best clustering with cluster head also enhance the energy level of node and improved lifetime of networks. Simulation results have shown the improved performance of proposed task.

Underwater Acoustic Sensor Networks (UASN) have a wide variety of applications such as oil platform monitoring, prediction of natural disasters, monitoring the pollution levels, study of aquatic life, etc. The next step after the deployment of sensor nodes in the subsea environment is their localization. Localization is a vital requirement to utilize the sensed data effectively and it is necessary for trailing of nodes and detection of the target. In this paper, drifting of unlocalized nodes caused by changes in ocean currents or other factors that are modelled by using Meandering Current Mobility (MCM) Model and the unlocalized nodes computes their positional coordinates by the Modified Dive and Rise Technique (MDRT) incorporating Enhanced Weighted Centroid Localization (EWCL) algorithm. Simulation results indicate that the proposed MDRT with EWCL algorithm outperforms the existing MDRT with DV Hop localization algorithm in terms of packet delivery ratio, delay, localization ratio, and coverage.

Wireless Sensor Networks (WSN) are characterized by highly application-specific nature, stringent resource constraints, self-organizing, spatio-temporal traffic, and large dynamic topology with several contradicting design goals. Of these design goals, network life time and energy efficiency are considered as of paramount importance. Many research works from the past have dedicated themselves in extending the network life time and achieving energy efficiency of WSN through various techniques, including that of the application of Tree as a data structure. This article attempts to present a detailed survey of the existing research works with the application of different variants of Trees. Further, the paper tries to analyze the performance implications of application of variants of trees, advantages, and disadvantages. The paper mentions possible feasibility of the application of Red Black Trees (RBL) in WSN and the potentials for future research while giving algorithmic comparison of RBL with other tree data structures.

The evolving of advance cyber threats requires the cyber security specialist and system analyst to detect, analyse and timely react against such kind of cyber attacks. In real practical scenario, the timely dissemination of attack information is a challenge and that is not possible without cyber threat intelligence with inclusion of deep analysis of attack features and attack contextual information. In this paper, automated proactive approach for cyber threat intelligence generation is presented integrated with standard data sharing formats that can act as attack indicator for the security defence mechanism put in place in an organization such as SIEM. The strength of Honeypot-based approaches for cyber threat intelligence is proven with well-defined use cases. The capabilities of Honeypots to detect zero-day attacks can be benefited if and only if the attack events are timely digested by the security solutions and that is only possible by sharing the attack events in standard data sharing languages. The developed system is fully automated that include captured attack data is processed by various automated analysis engines, augmenting the contextual information and applying deep learning models for later threat prediction. Finally, we propose a system design incorporating deep learning neural network-based cyber threat intelligence generation for cyber threat prediction. To achieve all these, cluster of VM Honeypots are deployed in a public IP4 network.

Multihomed devices are common in today’s environment but are underutilized. Uninterrupted application requirements have leap bounds in terms of throughput requirements. Multipath TCP (MPTCP) is a recent and successfully built standard at transport layer, to achieve the above requirement using multipathing. Long-lived flows carry heavy payload and short-lived flows look for quick response. Scheduling algorithm should consider these requirements and accordingly implement varying strategies to fulfill these needs. Long-lived flows need MPTCP, to get maximum throughput. Short-lived flows can perform with TCP or with slow subpath of MPTCP. To distinguish between short- and long-lived flows and distribute their traffic on appropriate subflow of MPTCP, an intelligent packet scheduling algorithm is required. Research is climbing toward building optimum scheduler for MPTCP. Many packet scheduling algorithms are investigated in this paper for proper path selection, increased throughput, energy efficiency, bandwidth aggregation and receiver buffer optimization, by which issues are listed for them to develop better strategy using newer and advanced algorithms.

As people life becomes easier, people are habituated to the comforts in order to meet their needs. One such invention is home automation. This area has many opportunities which are emerging every day. The proposed technique will give home automation with a cost-effective implementation. We can automate the lights, fans, refrigerator, etc. The requirements are NodeMcu, relay module, smartphone with Blynk and IFTTT apps, and proper Wi-Fi source.

Automation is increasing rapidly, and intelligence in applications emerges as a new form of automation. The impact of automation is observed in the software, hardware, and machine layer. Due to automation, human intervention is reduced in a number of areas such as manufacturing, transport, utilities, defense, facilities, operations and lately, information technology. With this view in mind, we have developed library system for renewal and submission of books. To develop this system, we have used a microcontroller, a barcode scanner, and conveyor. First, we developed graphical user interface with MATLAB and then with LabVIEW for this system. Developed library system allows students to renew and submit in 24/7 basis.

Internet of Things (IoT) has given promising chances to make better condition for humankind. One of its applications is smart metering. In today over-polluted world drinking water is the most precious and one of the costliest resources. This paper discusses the approach to provide access to clean drinking water for everyone cost effectively, by renting the smart water purifier and allowing the user to pay as per their usage, without worrying about the quality of water and maintenance of the purifier. This approach is win-win situation for the user as well as for the company. This system will be very economical.

Multimedia real-time applications like surveillance systems and video conferencing, where latency plays an important role along with the bandwidth. Today’s network advancements are able to resolve bandwidth constraints, but in terms of real-time applications issues related to latency impacts the performance. Multipath TCP (MPTCP) is observed to solve this issues and also able to give the better efficiency in network utilization. In this paper, we have attempted to compare and analyse the growth of MPTCP in multimedia applications and identify the related issues, where by solving these, performance of MPTCP will improve along with the increase in overall efficiency in multihomed or IoT devices and also on real-time multimedia applications.

We are living in a world where data is considered to be the next fuel; also, we know how much value the data is. Considering this over the health records on which a patient deals with whenever he/she goes to a hospital, the present scenario lags in securing the patients’ health record management system in order to provide more transparency over patients’ past health data. If the same was available, then data sharing between hospitals would have been much easier. If the hospital gets to know the past health history like the amount of drugs the patient is consuming as part of medication, then the doctor could make a stern decision over the disease and could also satisfy the patient with a better treatment over his symptoms. The basic problem to be dealt with these would be the privacy consideration of the patient when he/she is sharing the data. In order to tackle that problem, we are using a patient-centric health record management system under the distributed network architecture. In this paper, we are proposing a solution that makes use of the emerging blockchain (permissioned) technology to achieve this goal.

The Internet is an immense source of information. People use search engines to find desired web pages. All these web pages are gathered from the search engine by using web crawler. In traditional crawler, the information retrieval was based on the occurrence of keywords in a document due to which many irrelevant web pages were also retrieved. For the effective classification of web pages, support vector machine (SVM)-based crawler model is proposed in this paper. Various features of URL and web page are used for effective classification. SVM is trained by using these features and further tested. The proposed model is analyzed using precision and recall metrics. The experimental results exhibit optimized results by using this proposed approach.

With the rise of newAsher, Vinit analyticalThakkar, Hardik methods toTambe, Suyog track andBhavathankar, Prasenjit predict various aspects of our lives, gathering data is as important as ever. The way an object moves indoors is an important aspect in terms of crowd flow management, retail marketing, and security fields. Wi-Fi has become a ubiquitous standard with almost every indoor space having some form of Wi-Fi device. This existing infrastructure is put into service to track an object based on received signal strength values. Received signal strength values from different access points form a unique fingerprint which can be used to recognize a location on the map. In order to make the system adaptive to change and drift in fingerprints, deep learning methods are used to convert the system from a static to a dynamic system. The proposed neural network model has the error of 1.3 m for predicting the x-coordinate and 1.8 m for the y-coordinate in the given experimental setup.

Crime investigation and counteractive action is a deliberate methodology for distinguishing and examining examples and patterns in crime. Our framework can foresee regions which have a high likelihood for crime event and can predict crime-prone regions. With the expanding approach of mechanized frameworks, crime information investigators can help the law authorization officers to accelerate the way toward identifying violations. Utilizing the idea of information mining, we can extract beforehand, uncertain valuable data from unstructured information. Crimes are a social aggravation and cost our general public beyond all doubt in a few different ways. Any study that can help in explaining crime quicker will pay for itself. About 10 percent of the criminals carry out about half of the violations. Here we utilize graph mining techniques for gathering information to distinguish the crime instances and accelerate the way toward enlightening crime. Graph mining is done with the help of identifying the structure of the graph to obtain frequent patterns of information. With the help of graph database, we could store the past criminal records and infer important information from it. Our project aims to store the data in a graph database and try to determine the important patterns on the graph which can be used to predict the regions which have a high probability of crime occurrence and can help the law enforcement officers to enhance the speed of the process of solving crimes.

In this study, we have followed a semi-supervised approach for the classification of optical burst switching (OBS) network traffics generated by the network’s edge nodes. We used expectation maximization (EM) technique for Gaussian mixture model (GMM) to obtain a probabilistic classification of the OBS nodes. For this purpose, we used a trustworthy OBS network dataset from UCI machine learning repository. Preprocessing and principal component analysis were applied to the dataset for arranging the data so that GMM can play its role fairly. Only 1% (10 samples) of labeled data from OBS dataset was used to initialize the parameters of GMM and the rest 99% was used for testing performance of the model. We found a maximum accuracy of 69.7% on the test data using just 1% labeled data with the tied covariance type of the constructed GMM. The significance of this result is that it shows the GMM can be used in classification of OBS networks and other similar networks in semi-supervised way when one has very few labeled data and when labeling a huge dataset is not feasible.

Major online sites such as Amazon, eBay, and Yahoo are now adopting Spark. Many organizations run Spark in thousands of nodes available in the clusters. Spark is a “rapid cluster computing” and a broader data processing platform. It has a thirsty and active open-source community. Spark core is the Apache Spark kernel. We discuss in this paper the use and applications of Apache Spark, the mainstream of popular organization. These organizations extract, collect event data from the users’ daily use, and engage in real-time interactions with such data. As a result, Apache Spark is a big data next-generation tool. It offers both batch and streaming capabilities to process data more quickly.

Artificial intelligence (AI) is a rapidly growing field of computer science, which involves computer programming and offers tremendous advantages in the military expert system, human intelligence development, and support. This paper describes the use of artificial intelligence in the military field. It shows how the natural language of processing, ontology, and a system based on knowledge is used to create a unified military system. It also shares an insight into the use of AI in the military field for autonomous weapons, land analysis, and aircraft carrier landing.

Cloud computing is a label for the delivery of hosted services on the Internet. The first challenge in the cloud computing is security, and the second challenge is the large size of the file stored on the cloud. Today, cloud is in young age and used too much for storing the data. So, security is a major concern. Security endures a primary concern for businesses regarding cloud selection, usually public cloud selection. Public cloud service providers share their hardware infrastructure among the numerous customers, as the public cloud has an environment of multi-tenant. Multi-tenancy is an important feature of the cloud computing but also is prone to several vulnerabilities. From the end-user aspect, cloud computing looks very insecure from the perspective of privacy. In this paper, a technique REM (RSA algorithm, Elgamal algorithm, MD5 technique) is proposed by which we can provide better security to our data over the cloud. Unlike the previous techniques, which maintained only one pillar of security, this paper maintains two pillars of security, i.e., confidentiality and integrity along with reducing the size of the file.

The Internet of things (IoT) technology has reached great heights with the integration of efficient networking strategies, application protocols and VLSI. With the unlimited number of devices connected to the IoT, security has become a major concern. Mainly security is ensured by network and application protocols. But with rising threats, there is a need to lay emphasis on security from a hardware design point of view. Smart devices such as sensors and actuators are connected at the node of IoT and work on an embedded processing platform. Hence, embedded processors become an integral constituent of an IoT network. Selection of processors and identification of critical modules in the processor architecture play a key role in developing a secure design. Memory design will play a pivotal role in embedded system security. Memory attack accounts for loss or alteration of information which can impair the growth of IoT. This paper attempts to highlight security from a hardware perspective along with the potential threats likely to affect the critical modules.

Intrusion detection has become a major need for the current networked environment due to the high usage levels and the mandatory security that is needed, as sensitive information are being shared in the network. However, there exist several intrinsic issues in the network data that complicates the detection process. Further, real-time detection is also required due to the high velocity of data flow that can be expected in the domain. This paper presents an ensemble-based intrusion detection model to handle data imbalance and noise. Further, the entire approach has been decentralized to enable parallelized detection. The proposed model utilizes a BAgged Stacking Ensemble (BASE) as the detection model. The ensemble architecture initially creates data bags, enabling distributed processing. The bags are processed by multiple heterogeneous base learners. Prediction results from the base learners are passed to a stacked classifier for final predictions. This ensemble model is distributed over the network to enable decentralized processing. Experiments were performed on the NSL-KDD data and the results were compared with recent models. Comparisons with state-of-the-art models indicate the effectiveness of the proposed model.

The three-dimension printer means the three-dimensional printing which is also known as additive manufacturing and it is the advanced manufacturing tool used for producing the most complex shape geometries. We can produce complex geometry shapes without using any kind of computer tools. The three-dimensioned printer is used in many fields in the industries because of its most creating and functioning prototype with minimum human resources and time. 3D printer is used in automobile, architecture, engineering, education, medical industries, and civil engineering. It is most reliable, cost-effective and real-time application. The three-dimension printer is widely used and it is a very interesting technology to look out for.

The objective of this paper is to look at a system based on artificial intelligence that recognizes the faces and the system that processes the natural language. The process of face recognition has three phases: face representation, removal of features, and classification. But the most important phase-out of every phase is extraction. At this stage, the image is extracted from the unique features of the face image. However, variation in appearance remains one of the main factors affecting the accuracy of face recognition systems.

Presently, online networking have become a part and parcel of almost everybody’s life. Also, it has become major medium of personal and commercial communication. The current popularity of web technologies and social networking has made mandatory for a person to be active on these sites. Therefore, people became closely attached and find a medium to express their feeling, opinions, and emotions through these sites and often share information without bothering what they are sharing and with whom. Such context has become an avenue for cyberbullying. Thus, Internet is a platform for using abusive text or image, which may lead to many problems. Hence, it is important to place an effective system in order to put an end to such activities, through text mining techniques, machine learning, and natural language processing. In this paper, we compare different method of prototype implemented in the detection of abusive content.

Third-party IP cores, outsourcing of IC fabrication to untrusted foundries, have increased the vulnerabilities in IC’s and reduced the trust factor of a designer on the manufactured chips. These vulnerabilities are a consequence of malicious modifications of the original design, which have the potential to cause catastrophic damage to the system which uses these IC’s. IoT networks require the least vulnerable and highly trustworthy IC’s. We present a detailed study of such malicious insertions. Next, we discuss the methods for their identification and we also propose some countermeasures from a VLSI aspect.

Detection is one of the biggest threats to the organization. The detection of abnormal behaviors is one of the most difficult tasks for administrators of information systems (IS). Anomaly behavior is defined as any behavior that deviates from normal within or outside the organization IS, including insider attacks and any behavior that threatens the confidentiality, integrity, and availability of information systems for organizations. The detection of anomalies is extremely important to prevent and reduce illegal activities and to provide an effective emergency response.

The Internet of things plays a crucial role in all the fields such as agriculture, health care, wearable’s, industrial IoT (IIoT), retail, smart city and smart home. In every field, sensors are used to monitor the device values and provided to alert to the decision-making process. In the proposed Monitoring and Alerting using IoT in Cloud platform (MAIC) work, we predict whether the sensor is sending values based on the predefined time schedule. Changes in the agriculture environment have to be captured by these sensors and should be updated in a regular phase. If not, something would have happened to sensors, and such anomaly can be detected easily with the help of ThingSpeak open-source IoT cloud and its apps. In addition, sensor values (like temperature, moisture, humidity, etc.) need to be validated to reflect the correct measures as per the environmental circumstances. If one of these anomalies is detected, smart alerts could be sent to mobile or Twitter or an e-mail account possessed by the individual involved in agriculture activities. The sensor values captured in cloud are processed, and if any drastic change is detected, instant alert is sent to the individuals. These alert messages are authenticated by performing appropriate tweet analysis using R. The MAIC perms an intelligent monitoring of sensor values, and it gives entire belief to the experts who are involved in the system.

Hyperspectral image (HSI) classificationNirmal, S. attracted lotsSowmya, V. of attentionSoman, K. P. due to its complexity in dealing with large dimensions. In recent years, the techniques for dealing with the HSI have been evolved, ensuring the increase in efficiency to some extent in classification and other perspectives. Domain adaptation is a well-established technique for using any trained classification model, when the feature space from target domain is a subset of feature space from source domain. The objective of this paper is to create an efficient and effective model for HSI classification by implementing open set (OS) domain adaptation and generative adversarial network (GAN). This has advantages in quite few ways, such as creating a single training model that deals with various HSI data set with common classes, classifying the features in any data to specific trained classes and unknown (to be labelled) making it easy to annotate. The proposed open set domain adaptation for HSI classification is evaluated using Salinas and Pavia. The proposed method resulted in the classification accuracy for unknown classes as 99.07% for Salinas and 81.65% for Pavia.

Providing seamless connectivity is the major challenge in vehicular communication. The vehicles need to perform vertical handover in order to select the best target network. To identify the target network which satisfy the quality of service (QoS) demands is the need of the hour. To provide solution for this issue, a two stage fuzzy logic based target network selection is proposed where factor for handover estimation is carried out in first stage and fuzzy VIKOR based target network selection is developed. Through simulations, the ranking method is compared with other schemes.

Web content credibility is a measure of believable and trustworthy of the web content that is perceived. Content can turn out to be unreliable if it is not up-to-date and it is not measured for quality or accuracy and therefore, web content credibility is important for the individuals to access the content or information. The analysis of content credibility is an important and challenging task as the content credibility is expressed on essential factors. This paper focus on building predictive models to discover and evaluate credibility of a web page content through machine learning technique. A corpus of 300 web page contents have been developed and the factors like Readability, Freshness, Duplicate Content are defined and captured to model the credibility of web content. Two different labeling such as binary labeling and numeric labeling are used for defining credibility. In case of binary labeling, the high and low credibility of web content are represented by 1 and 0, respectively, whereas in case of numeric labeling five-point scale rating is used to mark the content credibility. Accordingly, two independent datasets have been developed. Different regression algorithms such as Linear Regression, Logistic Regression, Support Vector Regression (SVR) are employed for building the predictive models. Various experiments have been carried out using two different datasets and the performance analysis shows that the Logistic Regression model outperforms well when compared to other prediction algorithms.

In present day, an ever-increasing number of researches in information mining increases the seriousness about security issue. The security issues in information mining can’t just be tended by limiting data integration or by reducing the utilization of information technology. So as to keep up the security of the customer in the process of data mining, different types of strategies have been proposed that are dependent on the probabilistic perturbation of information records. Information mining service requires precise information for their outcomes to be significant; however, protection concern may impact clients to give fake data. Here, we present a detailed survey on privacy and security issues on data mining by analyzing different techniques from standard publishers of the year from 2010 to 2017. Based on the techniques utilized and types of issues are analyzed and classified. Moreover, to indicate the improvement and accuracy of all the research articles is also discussed. Furthermore, the analysis is carried to find the importance of their approaches so that we can develop a new technique to solve the security threats.

This paper’s main objective is to investigate the different types of robots, their operation, and application. Different types of robots follow different methods such as mobility, weight balance, distributed control system, biopsy, current 3G technology, skid steering, robotic weeding, calculation of Godspeed, suction cups, and network communication protocol. Here we discuss various types of robots, the following methods, the parameters used and their use in different fields. Finally, we can conclude that this paper briefly introduces robotic applicability in various areas.

According to the current situation of high demand for mobile applications, combined with the mainstream and the enterprise activities (Yang and Zhang in Computing technology, industrial information integration, [1]), the emergence of E-commerce has been playing a vital role in each and every sector of our daily lives. The number of new and different product developments increasing each year, triggers probability of previous technology users, especially in electronic gadgets. There are at least 40% of people who change their smartphones, laptops or any other electronic devices in a minimum of 2–3 years (by social interactions). Hence the cost of living is increasing steadily. For instance, cost of living in Bangalore in the year 2015 was around 5000Rs–6000Rs, and now it has been increased around to 7000–10,000 per month ((B)Grady Booch Rational Santa Clara, California in object-oriented analysis and design, [2]). Hence, Rental applications may reduce cost of living without affecting the mandatory life choices and their preferring lifestyle. This paper gives a detailed hands-on experience with the Rental Platform, UVRento, and how it can be implemented using E-commerce model application services. UVRento is an application developed using Ionic Framework with Firebase as its backend. Ionic being a cross-platform framework, it makes easier to develop powerful and efficient applications for every major platform running out there (iOS, Android, Windows Phone, etc.). While Firebase along with Google’s security provides the database facility which is more robust, more reliable and easy to integrate with Ionic Framework (Ionic framework document, [3]; Learn firebase—tutorials, [4]).

This paper explains about the Autonomous Unmanned Ground Vehicle named Lakshman, which has the following features—carry payload (food and medicines), follow the soldier like a companion, autonomous return to the base and it can also locate an enemy, etc. The vehicle can be remotely controlled if required. The technology been targeted are limited cross-county mobility, autonomous following techniques, localization, perception and path planning. It can carry stores and ammunition for three days. The implementation setup is been divided into four sections. They are mechanical, power management, core control, and autonomous section. Lakshman can work continuously for three days. The frame structure is made using mild steel to carry a payload around 200 kg. Power management unit makes the device to sustain more working hours, making it reliable and flexible to the requirement. The core control section controls the entire process and collects data from various sensors. Autonomous section controlled by ARM cortex A53 to process high data speeds. The most important future plan is to provide steganography method of communication, which is a highly secured communication, so the vehicle cannot be easily tracked by other people with enhanced technology. By implementing a dedicated FPGA for encrypted steganography communication will provide greater assist to the A-UGV. Thus Lakshman can be a very effective tool to enhance Indian Defence Strategies.

Cloud Computing is one of the rapidly growing areas in the field of computer science. A modern paradigm provides services through the Internet. An Internet-based technology employs pay-as-you-go model (PAYG). Load balancing is one of the important and vital issues in cloud computing. It (load balancing) is a technique which improves the distribution of workloads across various nodes. Load balancing distributes dynamic workload among various nodes so that no specific node breaks down by heavy load. It is crucial to utilize the full resources of a parallel and distributed system. Resource consumption and energy consumption both can be limited by distributing the load evenly and properly using different load balancing techniques. Lowering the use of resources increases the overall working performance of the system thus cutting down the carbon emission rate and providing the greener and safer environment. Different algorithms and techniques are employed to balance the load on nodes. These techniques can be examined on different parameters such as resource utilization, reliability of the system, system performance, related overhead of the system, power saving feature, scalability and many more. This paper presents the insight into the existing load balancing algorithms and their comparison on basis of different parameters.

We show that vertices part of a physical cluster (determined per the edges that connect the vertices) in a complex real-world network need not be similar on the basis of the values incurred for node-level metrics (say, centrality metrics). We adapt a recently proposed approach (based on unit-disk graphs) to determine logical clusters comprising of vertices of similar values for node-level metrics, but need not be physically connected to each other. We use the Louvain algorithm to determine both the physical and logical clusters on the respective graphs. We employ the Silhouette Index measure to evaluate the similarity of the vertices in the physical and logical clusters. When tested on a suite of 50 social and biological network graphs on the basis of neighborhood and/or shortest path-driven centrality metrics, we observe the Silhouette Index of the logical clusters to be significantly larger than that of the physical clusters.

In wireless modulation technologies, it has advanced frequency modulation. It is capable of performing in Quality of Service (Qos) for utilizing the scope of mobile ad hoc. It is measured only for calculating the ratio between two nodes. It will form an implementation of single-hop delay. After that, to construct the multicast tree for real-time in delay-sensitive tree, proposed model has been established in MANET. While increasing a network in capacity, this proposed multicast protocol can be minimized in the host and in a particular time the transmission node can block the entire neighboring node so that there is no interrupt and wastage of data that can be occurred in the data for properly adjusting data rates. Simulating results provides lot of accuracy. It induces the expected result at the end of this paper.

Supervisory control and data acquisition (SCADA) systems monitor and control the critical infrastructures (CI) such as power generation, smart grids, oil–gas pipelines, wastewater management, and nuclear power plant. Due to the drastic increase in cyber attacks, maintaining SCADA systems has become a complex task. Difficulty in securing the SCADA has gained the attention of researchers in designing a robust intrusion detection system (IDS). However, existing machine-learning and statistical approaches fail to detect the cyber physical attacks with high detection rate. This paper presents a sine-cosine optimization based recurrent neural network (SCO-RNN) to detect the cyber physical attacks against SCADA systems and the performance of the proposed SCO-RNN was validated using the Secure Water Treatment (SWaT) dataset in terms of accuracy and detection rate.

Supervisory Control and Data Acquisition (SCADA) systems play a prominent role in monitoring and controlling the Critical Infrastructures (CIs) such as water distribution, nuclear plants, and chemical industries. On the other hand, SCADA systems are highly exposed to new vulnerabilities as it highly relies on the internet. Machine learning approaches have been employed to detect the cyberattacks injected by the attackers in CIs. However, those approaches failed to protect the CIs against the ever-advancing nature of cyberattacks. This work presents Salp Swarm Optimization-based Isolation Forest (SSO-IF) to build an efficient SCADA intrusion detection system, and the experiments were carried out using power system dataset from Mississippi State University. The performance of SSO-IF was validated over the state-of-the-art intrusion detection techniques in terms of classification accuracy and detection rate.

Dynamic data-driven simulation is introduced to improve the scheduling performance. The customer will need a just in delivery of service scheduling. Mathematical model of the scheduling problem is constructed, and a scheduling method is proposed to improve the performance of scheduling. Four different optimizations for the dynamic cloud manufacturing scheduling problems are presented in this paper, namely average service utilization rate, average task delay time, weighted average task delay time, proportion of delay tasks and constraints. The scheduling strategies are constructed and simulated in the SIMSO software.

One of the fundamental problems the world facing today is waste management. The main problem with waste management is that the dustbins placed by the municipal company are filled earlier and overflow before the next cleaning process (Mirchandani in 2017 International conference on big data, IoT and data science (BID), pp 73–76, 2017 [1]). This poses environmental threats and causes health problems. This dangerous scenario can be avoided by installing an alarm system for the dumping of solid waste. The main objective of this paper is to alert the municipal web server to clearance of overloaded dustbins when solid waste flows over the public dustbin. Here, we can build the dustbins using the MCU, RFID, GPS, ultrasonic sensor and Wi-Fi module to prevent the overflow of the dustbins.

Today, majorityJose, Christy James of theRajasree, M. S. world population has one companion with them always. This companion has all the private information of the person. That companion is the smart phone. Considering the private data its holding, the unauthorized usage by friends, family members and strangers are to be avoided. The traditional way of preventing the unauthorized usage is to lock the device with PIN numbers, graphical patterns, fingerprint sensors and face unlock mechanisms [1]. The last two are available with high-end phones only. Even these mechanisms rely on the first-mentioned methods in case of a sensor failure. Primary authentication mechanisms could be easily breached. In this paper, we are investigating the usage of handwritten signatures as an authentication method. We are suggesting a Pseudo signature, a signature drawn on the touch screen of the phone using the finger tip. We have used convolutional neural network classifiers to classify the genuine user and intruder. Experimental results show our suggestion is promising and it could be used as an easy-to-use user-friendly primary authentication for smart phones.

Generally, the real-world environment is dynamic in nature. The transitory and individuals items seem stationary for some time; however, they are later moved, for example, chairs. A robot must somehow manoeuvre through the moving objects for which it uses the SLAM algorithm. This paper explores Gmapping and HectorSLAM on autonomous mobile robots for indoor applications using Microsoft Kinect Robot Operating System (ROS).

Nowadays, predictive analytics is one of the most important big data trends. Predictive analytics is the accumulation of extensive, mostly unstructured data from various sources. The mixture of various information sources, for example, online networking information, climate and traffic are improved by internal information is especially basic. But both predictive analysis and data mining attempt to make divination about possible events in the future with the help of data models. Predictive analytics processes utilize various statistical strategies such as machine learning or neural networks, regression and extrapolation to perceive in the information patterns and infer algorithm. These algorithmic procedures are assessed depending on test data and optimized data. It is to be noted that as data availability increases, the accuracy of the algorithm also improved. By chance if the improvement procedure is finished, the algorithm and the model can be connected to information whose classification is obscure. Predictive analytics model captures connection between various factors to assess chance with a specific set of conditions to distribute a score or weightage. Successfully, on applying predictive examination, the organizations can adequately explain huge information for their benefit. We present a detailed survey on data mining and predictive analytics here, by analyzing 15 techniques from standard publishers (IEEE, Elsevier, Springer, etc.) of the year from 2008 to 2018. Based on the algorithms and methods utilized which are inconvenient, the problems are analyzed and classified. Moreover, to indicate the improvement and accuracy of all the research articles is also discussed. Furthermore, the analysis is carried to find the essential for their approaches so that we can develop a new technique to previse the future data. Eventually, some of the research issues are also inscribed to precede further research on the similar direction.

We are often directed to switch off our mobile phones and other electronic devices while traveling in a plane due to the interference caused by the radio transmitters installed in these devices. As a result, most customers do not enjoy the benefit of data on-board except for some airlines who provide the paid data services. Also, due to the recent outburst in the demand for data, there is soon going to be a congestion of the radio frequency bands. Thus, developing a technology which can entirely replace the current system of radio frequency-based data transmission is beneficiary [1]. Light fidelity (Li-Fi) is a next-generation technology which uses visible light as a medium for transmitting data [2]. Thus, it serves this increasing demand for data due to its vast bandwidth and does not interfere with airplanes as well. This paper aims on demonstrating the application of light fidelity (Li-Fi) in the entertainment services provided inside the airplanes. All the fundamental principle of visible light communication (VLC), the modulation techniques involved as well as how a circuit can be realized for transmitting data in an airplane using those principles have been discussed in this paper. Light fidelity (Li-Fi) when used to its full potential can certainly prove that everyone can count on it when RF fails to serve the purpose.

Recorded biomedical statistics are utilized for predicting various syndromes in humans. Recorded electrical activity of heart can be used for predicting cardiovascular ailment likelihood. Several steps are involved to process biomedical signals, among which the first step related to pre-processing, in which a noisy signal is processed for generating noise-free signal, which can be utilized for further operations. This work gives a detailed understanding of de-noising techniques those have been used for the last decade, for cardiac signals. These techniques utilize the benefits of discrete wavelet transforms (DWT), Bayesian approach, singular value decomposition (SVD), artificial neural networks (ANN), empirical-mode decomposition (EMD), adaptive filtering, and finite impulse response (FIR) filtering. These techniques have been implemented for de-noising of biosignals, individually as well as combining with other techniques, for better results.

Process scheduling plays a vital role in multitasking for any operating system. There are many factors involved during process scheduling like priorities, free memory, user demand and processor which if not handled properly can be very complex and time consuming. Neural network has adaptive nature which can be used to handle the complex part easily. The main aim of this paper is to review different types of scheduling algorithms working on the principle of neural network and offer constructive criticism to improve their efficiency.

Cross-layer procedure integrates functionalities from first layer of OSI model (physical layer) to transport layer. It enables flexibility, trustworthy and effectiveness in communication process. In this approach, it collects system parameters from multiple layers to enhance the capability of the network. The standard level is decreased by enabling flexibility through inter-layer information exchange. The node selection mechanism is done through fuzzy logic system to provide an efficient communication. Among these benefits, the cross-layering approach faces a problem with security threats in a network. To mitigate these attacks in a network, a trust based cross-layering framework (T-XLM) initiates a trust estimation mechanism using fuzzy logic system to articulate approximate experimental knowledge which is used in reputation building in nodes to avoid defaults in future actions. The TRUFIX is a T-XLM based protocol which is used to permit and hold inter-layer data exchange to accommodate traffic awareness and improve system version. The extension of TRUFIX is E-TRUFIX in which the node if it identifies a malicious node it takes an alternate neighbor route and sends the packet toward the destination. By taking into account with simulation results, E-TRUFIX was compared with FUGEF and TRUFIX which shows an increment in the packet delivery ratio and delay due to the alternate neighbor route.

Applications of IoT are endless, and it has been into many fields like wearable devices, home appliances, etc. This paper investigates the smart parking management application domain, in which the focus is on the users (user requesting for the parking) privacy. Since the number of devices connected to the IoT network grows exponentially, security of the user connected to IoT network is of paramount importance. A scenario like hacking the confidentiality of high-profile users can happen. Thus, this system proposes a solution to protect the identity of the user’s by averting the exchange of private information by adapting the zero-knowledge protocol (ZKP) with elliptic curve cryptography (ECC) implementation. ECC, compared with other public-key crypto algorithms, is the best choice for cryptographic implementation on resource-constrained devices. In this framework, the MQTT protocol has been used to establish efficient communication between the user and parking system.

Research in the field of Sentiment analysis is blowing day by day exponentially in the recent past and currently it is the much-acknowledged discipline. This is due to the outpouring users of internet and thus generated effusion of data in the form of reviews, comments, blogs, communications, etc. These are treasure trove of information needed to comprehend the fact-based opinions of diverse consumers. Elucidating those contents is propitious to various stakeholders as right sentiments can be gathered through it. But trading with such ginormous unstructured inputs embodies diverse challenges. These challenges trigger the raise in desideratum and thus summon the need to instigate pioneering logics to meet the same and to optimize the existing approaches. Sentiment analysis and opinion mining terms are used equivalently. This existed from ancient days. But the modes employed to perform this were different like in the form of surveys, elections, etc. It was carried out either by individuals or a group or an organization. Individuals used to consult friends or family before changing themselves from strangers to customers of any product or audience of any events. Groups or Organizations used to conduct surveys. But gradually most of the decisions were to be data-driven and thus appropriate decisions were to be made in short-run to bridge the gap between businesses and consumers and to gain the competitive advantage in market. So now this process was to be automated for mutual benefits by exploiting the escalation in technology. Various approaches are emerging every day to strengthen and ease the process. In the same direction, this paper addresses how sentiment analysis of online reviews can serve as suggestions to strangers and help them better in gaining additional insights to make well-judged decisions or choice by visualizing the analysis in the graph form.

Co-occupant DoS assaults are discovered as most defenseless dangers in relation to distributed computing which is an asset and generally is obligatory in nature. Co-inhabitant DoS assaults would deplete out the cloud assets which impair the certifiable cloud clients from executing cloud utilization. Consequently, aversion and conclusion of the event related to co-inhabitant assaults result in fundamental assignment of cloud form. In our past research strategy, two-player game approach (TPGA) is acquainted which points that show evasion of co-inhabitant DoS assaults by learning and arranging the virtual machine requested from clients on account of the low, medium, and high hazard demands. Anyway, this strategy decreased in its execution regarding discovery of hazard dimension of client ask for VMs. There is no predefined method for recognizing hazard estimation of VM assets. And furthermore, it would be troublesome to keep up and refresh the hazard status data of the cloud assets. This is settled in the proposed research strategy by presenting the system called status monitoring system-based defense mechanism (SMS-BDM) or state observation-based co-resident DoS attack detection (SO-CRDoS-AD). In the proposed research technique, at first, hazard estimation of each asset dependent on client demands is assessed by utilizing hazard recognition metric. In light of this hazard metric esteem, state estimation of each client asks for as far as VM is refreshed. The states that are considered in this work are security state, vulnerability state, attacked state, positive state, negative state, degenerate state, and failure state. These state estimations of VM assets are refreshed occasionally with the assistance of Markov chain display. This examination technique is actualized in the CloudSim condition from which it is demonstrated that the proposed research strategy can guarantee the exact recognition of assault status of VM asks for; therefore, the security level can be upgraded.

Nowadays, resource scheduling in cloud environment is a challenging task as the number of customers increases for utilizing the cloud services. In this cloud environment, allocation of suitable resources to the corresponding VM depends on the QoS requirement of the specified applications. Researchers have developed so many resource scheduling algorithms. However, the service providers in cloud environment still find it difficult to choose the appropriate algorithm for their applications. This is due to the heterogeneity of resource types, interdependencies, uncertainty and dispersion of assets in the cloud environment. This paper reviews all the available load balancing algorithms in a nutshell.

Healthcare system providing cloud-based storage makes possible to store the patient’s therapeutic records to the remote server than keeping the files and radiological images on a hard drive or local storage device which enables the patient to access their medical records at any place from the Internet through a web-based application. The structure of cloud application guarantees the privacy and security of health-related data to preserve the sensitive health information. The architectural design of cloud computing is to alleviate the privacy concern and to fulfill the confidence and trust of the cloud-based healthcare organization. This work proposes a structure for a cloud-based healthcare system to allow patients get to their medical images and reports from the cloud, ensuring that the data are available when it meets the prerequisite of a particular contract that is authorized. The requirements, architecture design, software components, and validation methods of cloud-based healthcare system are introduced.

In this Internet era, the digital communication is unavoidable, and its contribution to the development is significant. Due to the enormous data communication, there is always a question about the security of the data. In this paper, a hybrid reversible data hiding method is proposed to transmit the data in secure manner through video files. Secret data like one-time password (OTP) can be embedded into one of the frames in the video. This achieves more security than the regular ways. The proposed method has compared with mean square error (MSE) and peak signal-noise ratio (PSNR).

Security and dependable transmission is testing errand in portable ad hoc system along the portability of system gadget bargained with assault and loss of information. For the aversion of assault and dependable transmission, different creators proposed a technique for verified directing convention, for example, SAODV and SBRP (secured backup routing protocol). The procedure of these strategies works alongside route disclosure and route maintenance, inventing, and route prolong kept up required more power utilization for that procedure. For the verification of gathering hub, gathering mark procedure is utilized, and rest mode edge idea is utilized for power minimization. Our proposed strategy is reenacted in ns-2 and contrast with other directing convention gives a superior act in contrast with vitality utilization and throughput of system. There are various approaches to compute trust for a node such as fuzzy trust approach, trust administration approach, and hybrid approach. Adaptive information dissemination (AID) is a mechanism which ensures the packets in a specific transmission, and it analyzes if there are any attacks by hackers. It encompasses of ensuring the packet count and route detection between source and destination with trusted path. Trust calculation based on the particular condition or context of a node, by sharing the context data onto the other nodes in the system would give a better solution to this problem. Here, we present a review on different trust association approaches in MANETs. We bring out immediate response from the methodologies for building up trust of the taking part hubs in a dynamic and unsure MANET atmosphere.

On-line frequency allocation problem (FAP) for wireless linear cellular networks reusing the frequency of drop calls is studied. In this paper, FAP is investigated on ring networks. The coverage area of highway that surrounds a large mountain is divided into number of regular hexagonal regions called cells. Each of the cells is aligned with exactly two adjacent cells, thus forming a ring topology of the network. Base station (BS) which heads a cell is the transceiver of new or drop call requests from users of same or neighbouring cells. Utilization of spectrum must be properly managed so that no calls generated from same or adjacent cells should be left without getting services from the BSs. In the proposed on-line algorithm for FAP, the drop call releases frequency, and it is reallocated to an ongoing call using greedy strategy. The algorithm is also implemented on 2- or 3-colourable graph model of ring depending on even or odd number of cells that constitute the network, respectively. The performance of the algorithm is analysed on 2-colourable ring network, and it is found that the competitive ratio of the algorithm is $$ \chi /2 $$, where the chromatic number of ring network is $$ \chi $$. It has been also shown that there is no on-line algorithm of less than 1 and $$ 3/2 $$-competitive ratios for FAP in 2-colourable and 3-colourable ring networks, respectively.

Growing population and continuous increase in health issues increases the significance on the healthcare centers which are demanding for an efficient healthcare system. 24/7 clinical service is not available to all common people and many life losses occurs due to unavailability of medicine on time and many researches were made to make the medicine globalized. We propose an IoT based module where the health-related parameters of patients are recorded using sensors, symptoms computed are uploaded to the server, by comparing the computed symptoms to the database the problem is detected and the medicine and dosage level for the detected health problem is prescribed automatically. Database must be created by group of authorized specialist doctors and the database management can be done with the help of SQL server.

Among the various coding techniques, polar codes are very useful, since it achieves the ultimate channel coding characteristics. In this paper, a decoding architecture is proposed which reduces the memory consumption and delay. To overcome the problem of concurrency, a parallel processing is done. The further research is in processing on the polar codes to be applied for next-generation applications. Polar code is preferred since it increases the speed of the operation for large number of bits (i.e.) for large code length. This implementation process is further extended by combining the encoding and decoding process by using radix-k based design.

The emerging 5G system has promising advances in the near future. The 5G system will be offering many features that are not being possessed in the past generations. To meet the several requirements, orthogonal frequency division multiplexing (OFDM) is a best choice. In the existing OFDM technique, model and framework of 4G LTE, chosen mainly for mobile broadband (MBB) service, are not that sensitive to recession or authenticity. Despite the fact that OFDM provides high spectrum efficiency through orthogonal frequency multiplexing, the OOBE (out-of-band emission) of OFDM is still not very satisfactory, and also, OFDM requires global synchronization which comes at the price of extra signaling. Indeed, when the user is perfectly synchronized both in time and frequency domain with the base station (BS), in terms of bit error rate (BER), the performance offered by OFDM is very good and resistance to the carrier frequency offset (CFO). These circumstances are energy costly as the user needs to exchange messages with the BS to ensure this synchronization. Therefore, if these conditions are not satisfied, the OFDM BER may be high. Nevertheless, the OFDM modulation suffers from high side lobes which decrease the spectral efficiency and create adjacent channel interferences. For these reasons, several MCM schemes have been developed these recent years as candidates for 5G systems such as filtered OFDM (F-OFDM). Filtered OFDM (F-OFDM) is an alternative to the OFDM modulation in 5G system. It offers all the advantages maintained by OFDM such as efficient performance and flexible frequency multiplexing that meets the needs of future generation. It also meets OOBE requirements and thus helps in efficient spectrum utilization.

Owing to the numerous advantages arising out of storing data in the cloud such as flexible costs, improved mobility, many users are outsourcing their data to the cloud. However, it poses security challenges and rigidity issues. The cloud is often honest but curious, and hence, the need to encrypt the data arises. However, it renders the basic processes like searching difficult. Hence, there is a need to implement search algorithms on encrypted data. Plaintext fuzzy search and semantic search techniques cannot be implemented on encrypted data. To save its resources, the cloud may return partially correct results, and thus, there is a need for verification of the results returned. Access control mechanisms for multiple users should be implemented ensuring the confidentiality of unauthorized data. This paper deals with the design and analysis of a privacy-preserving top k multi-keyword synonym/similarity fuzzy search.

Various communication applications from Internet video streaming to video broadcasting require high-speed throughput variable-length code decoder. The fundamental techniques (parallelism and pipelining) used to improve the speed of algorithm cannot be applied to VLC decoder, due to variable length of symbol codes and due to no boundary defined between symbols. Considering the basic algorithm of VLC decoder, the speed of decoder is improved by reducing the processing time while identifying the code word and corresponding code length from lookup table. The rearrangement of code words in code book is proposed in this paper to minimize the time. For variable-length code of 17 bits, the proposed design depicts an improvement of 46% in speed compared to traditional single length code book.

The automotive industry is progressing expeditiously. The popularity of electric vehicles (EV) has shot up over the last few years. As a matter of fact, electric drive systems such as DC motor drives, one of the electrical drives, are rapidly gaining prominence because of their high efficiency, better dynamic response and low upkeep. The following work deals with the drivetrain of the pre-existing electric motorcycle comprised of a brushed DC motor. In this work, a system is developed to maintain the speed of the electric motorcycle at a set speed, under various disturbances using PI control. A detailed mathematical model, transfer function and simulation of the same are obtained using the software package MATLAB, SIMULINK. The controlled DC motor is made to track a variable speed set point with zero steady-state error and desirable disturbance rejection capabilities.

To improve the copyright protection of the digital image, the watermarking technique is used. By changing the entropy, identify the location where the secret data gets embedded into the original image. It provides less distortion and high robustness. The watermarked image is send to the receiver through the wireless sensor network. In WSN, clustering is the best technique to save the energy. In cluster-based WSN, cluster head requires more energy to receive the data from the sensor nodes and transmitting it to the base station. To maintain the lifetime of WSN, the proper selection of CH is essential. In this paper, we propose the energy-based least squares multiple algorithms. Simulation results show that the ELSM algorithm is more efficient compared to the WLSM algorithm to improve the range of bandwidth, cluster overhead, skew rate, offset rate, carrier signal, reference signal and control output signal.

This study aims to design a model on mobile learning used by international students in tertiary education. It has used a survey questionnaire distributed through the LimeSurvey. A total of 12 international students from a tertiary education (6 female students and 6 male students) participated in Canada. The results revealed that male students got higher impact to the performance expectancy (PE), social factors (SF), and facilitating condition (FC) but on the other hand, female students got an impact on PE and FC. Furthermore, female students got a significant impact on PE and SF on behavioral intention (BI). Gender is having an impact on EE and FC. Finally, PE and SF have an impact on the BI to use mobile learning in tertiary education. These findings will help practitioners, educators, policymakers to implement mobile learning in tertiary education for international students in Canada and abroad.

Multiple devices interconnected with each other via the Internet are the key concept behind IoT. It allows autonomous devices with the possibility to use the Internet for communication and exchange of data. This paper focuses on monitoring the transformer in real-time fault detection and records distinct operating parameters of the transformer like voltage imbalance, load current, transformer oil levels, temperature, vibration. Based on these parameters, the transformers fail state (i.e. a state where transformer malfunctions or completely stops working) and health (i.e. voltage, current, oil levels, temperature and vibration) are predicted by making use of an artificial neural network (ANN) algorithm. Use of this technology can minimize working efforts, thereby improving accuracy, stability, efficiency. Thus, remote monitoring and machine control are achieved, as well as ANNs help to determine the performance and yield appropriate measures accordingly. In this case, sensors are used to sense the important parameters of equipment such as current, voltage oil level in any operating transformer. By analyzing relevant data using ANNs, this system will be beneficial in many industries. Likewise, this system is generalized to be used in a wide array of industrial automated machines.

The main objective of this research work is to deduce best among the existing search techniques. The various methods or the algorithms that we are going to discuss here are breadth-first search, depth-first search, and A* algorithm. Comparing various search techniques that are used in artificial intelligence problems for gaming applications.

Crowdsourcing permits huge scale and adaptable summon of human contribution for information social affair and examination, which presents another world view of information mining process. Customary information mining techniques frequently require specialists in investigative areas to comment on the information. In any case, it is costly and ordinarily takes a long time. Crowdsourcing empowers the utilization of heterogeneous foundation information from volunteers and circulates the explanation procedure to little segments of endeavors from various commitments. The data analytics process is done via logistic regression.

RDBMS is the most normal and antiquated way to deal with database arrangements. The data is kept in an extremely organized methodology in style of tables or relations. With appearance of Big Data in any case, the organized methodology misses the mark to serve the needs of monstrous data frameworks that are basically unstructured in nature. Expanding ability of SQL, however, allows huge amount of data to be overseen, and it does not generally make a difference as a response to Big Data frameworks that anticipates brisk reaction and quick speedy adaptability. To comprehend this disadvantage, a sensibly new database framework suggested to as NoSQL was presented. NoSQL framework is acquainted which give the fast adaptability and unstructured stage for Big Data application.

The goal of this project is to find whether a given medicine is fake or original using blockchain technology. Blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but also has the potential to disrupt other markets. It removes the need for trusted intermediaries, can facilitate faster transactions and add more transparency. A medical product is counterfeit when there is false representation in relation to its identity or source. In the case of medicines, the implications are more severe. This technology stops the entry of fake drugs into the supply chain, mainly the part between the manufacturer and consumer. The technology uses digital signature where each block gets a unique crypto id. This digital signature is provided for each block and it gives a strong control of ownership.

WSN plays a significant role in various fields like in communication, military, etc., and as it is being applied in various fields, the faults in it have to be taken seriously. There were different approaches taken to detect these faults. But, there were very less number of approaches of detection of faults through RSSI. There can be many reasons for change in the measured value of a node. But, change in RSSI can only be limited to distance, disturbance or due to the health of the node. Due to this reason, we are motivated that detection of fault in WSN using RSSI can be reliable than other methods in suitable scenarios. Also, a suitable machine learning technique has been implemented to reduce human involvement in the detection process.

Over the years, various attempts have been made to make social media and devices more disabled friendly. Screen readers and other assisting technologies have made it easier for people with visual disabilities like low vision and blindness to interact with social media. However, images on the social network are still inaccessible to them. Developing the description of an image in natural language is an upcoming problem which is at the intersection of disciplines of artificial intelligence, computer vision, and natural language processing. The image captioning task, also known as visual captioning, makes the technical chassis of many important applications of semantic visual search and visual questioning, photo and video sharing in social media, visual intelligence in chatting robots, and aid for visually impaired people to perceive surrounding objects and happenings. To describe an image effectively, it involves the detection of objects in images, identifying scenes and attributes of the objects. Then, these labels are used to construct semantically meaningful sentences to generate paragraphs which describe the images. Our research makes an attempt to carry out the task to produce a paragraph like a description of images.

The popularity of agents in artificial intelligence (AI) can be seen almost everywhere in the form of effective decision making, predictive analysis, expert systems, and so on. In parallel, wireless communication systems in the form of wireless local area networks (WLANs) and wireless personal area networks (WPANs) are also providing tremendous support for data communication. Exploring and utilizing the benefits of artificial intelligence and wireless communication systems together in an environment are still in the nascent stage due to the divergent characteristics of the respective domain. One of the major drawbacks in the existing wireless communication system is interference in 2.4 GHz band. This work proposes a multi-agent-based interference mitigation technique to mitigate the interference issues in wireless communication system and aims to enhance the communication process in terms of packet delivery ratio. This work is implemented as a pilot project in an educational institution for teaching students in wireless communication environment, the result shows that technology has added new dimensions to teaching–learning process, and performance of the wireless communication system is also increased in terms of packet delivery ratio.

The architecture is aimed at pushing the computing towards the edge. When most of the computation occurs towards the edge device where the data is generated, the processing becomes faster and more efficient. This improves the user’s wait time and delivers results faster to the user. Machine learning techniques are implemented in the edge devices. While one can process data at the sensor, what one can do is limited by the processing power available on each IoT device. Data is at the heart of an IoT architecture, and one needs to choose between immediacy and depth of insight when processing that data. The more immediate the need for information, the closer to the end devices your processing needs to be. We propose an architecture to use machine learning algorithms in the limited memory of the edge device.

Internet of things is enhancing various industries by connecting all devices to the Internet to solve key challenges. Transportation is a huge sector where extensive research and development is undergoing. The current developments by various independent corporate research companies use the power of cloud computing for storage and processing of data. With the need for quicker decision-making in critical services, a new paradigm, called edge computing is under research, where latency is tackled. Since, each has its own limitations, a new approach is given that involves both these paradigms—called hybrid computing. We would like to discuss the merits and demerits of each of these paradigms and propose two variants of hybrid computing with respect to smart transportation and smart tires in specific.

This paper presents an IoT-based low-cost aquaculture system for automatic monitoring of fish firms to increase the production of fishes in order to meet the protein demand. A microcontroller is employed in measuring several essential parameters of the water like pH, temperature, water level, oil layer, and to monitor the fish behavior to understand their hunger level that is directly affect in the growth of firm fishes. A mobile application and an interactive Web interface are designed to notify the measured parameters value and necessary recommendation. Bluetooth and ESP8266 Wi-Fi module are integrated with the system to deliver the data to the mobile app and Web interface. In case of any abnormality, the system informs the concerned authority to take the immediate steps. In addition, the forecasting of the several water parameters in the following day to take an earlier preventive action makes the proposed framework more exceptional. Therefore, the system will assist the fish farmer to enhance the production of quality fishes, which may help to meet the protein challenges for the large population.

In this paper, we have proposed and designed a hybrid system that is able to detect fire breakout, gas leakage and noise pollution as well as providing location of the affected area and opening fire extinguish system. Raspberry Pi is integrated with MQ-5 sensor, humidity sensor, flame sensor, sound sensor and camera module. A 360° servo motor is accumulated with camera module to capture affected location even at any angle. To increase the reliability of this system, an authorized person is assigned to assess the real situation. If fire is detected, camera module takes a snapshot of affected region and sends it to admin’s email through 802.11n LAN wireless module. Different sensors’ value also transmits to server through that module in one-minute interval. Moreover, a buzzer is activated in control room when data and picture is sent to admin. If admin confirms the incident, the system will raise the alarm in whole workplace, uncover the water valve of affected region and send message to the owner and nearby fire brigade. Thus, a garment can secure the workplace for its workers.

In the recent period, wireless sensor networks are mostly used in diverse applications of sensing that includes medical, armed forces, civil, adversity management, environmental and commercial applications. Wireless sensor networks typically comprise a hefty amount of sensors. Sensors are device that produces a measurable response in changing the environmental conditions like temperature, humidity, pressure, etc. As the sensor has the limited energy, to boost the duration of network and maintaining coverage preservation, we necessitate an approach that involves least sensors in communication of sensed data to base station. In this research work, we amalgamate the conception of genetics and extended search to evade gratuitous energy consumption which is due to activation of superfluous nodes. We aim to prepare a schedule in which least number of sensors are stimulated and cover each point of concerns.

Phishing is defined as imitating a creditable company’s website aiming to take private information of a user. These phishing websites are to obtain confidential information such as usernames, passwords, banking credentials and some other personal information. Website phishing is the act of attracting unsuspecting online users into revealing private and confidential information which can be used by the phisher in fraud, blackmail or other ways to negatively affect the users involved. In this research, an approach had been proposed to detect phishing websites by applying a different kind of algorithms and filters to achieve a reliable and accurate result. The experiments were performed on four machine learning algorithms, e.g., SMO, logistic regression and Naïve Bayes. Logistic regression classifiers were found to be the best classifier for the phishing website detection. In addition, the accuracy was enhanced when the filter had been applied to logistic regression algorithm.

As we can see in today’s world, only some devices like PCs and mobiles are connected to the Internet. Nowadays, the world is copiously surpassed by the Internet and the internet of things. The Internet is used for the rudimentary need of all human beings [3]. IoT represents the concurrence of advances in minimization, wireless connectivity, enhances batteries and data storage capacity, and without sensors, IoT is not possible. It simply means to monitor a physical device or machine, or it is inter-networking of physical devices which is entrenched with electronics, sensors, software, and network connectivity to facilitate it to achieve greater value and services by swapping data with the producer [1]. IoT permits objects to be sensed or controlled remotely across the network infrastructure. The result improves accuracy, economic benefits, efficiency and reduces the intervention of a human. In this paper, we are going to deal with rudimentary and imperative perceptions of IoT and its scope in the forthcoming future. This paper studies the need for IoT in day-to-day life for different applications and gives fleeting information about IoT. IoT contributes significantly toward revolutionary farming approaches. So, we are trying to demonstrate IoT in the automatic watering system [2]. An automatic watering system monitors and preserves the approximate moisture gratified in the soil. Raspberry Pi is used as a microcontroller to implement the control unit. The setup uses the temperature sensor, moisture sensor and humidity sensor which measure the approximate temperature, moisture and humidity in the land. This value empowers the system to use belonging quantity of water which avoids over/under irrigation.

Internet of Things (IoT) based networks with sensors are energy and delay stringent. Efficient scheduling algorithms for IoT-based networks are the need of the hour. Nodes with selfish behavior degrade the performance of the network. Hence, a scheduling algorithm that schedules packets based on their emergencies and priorities yields better results. In this paper, M/M/1 and M/M/N scheduling scheme to schedule Emergency packets (E-packets) and Regular packets (R-packets) is proposed. The next-hop nodes are chosen based on the trust value of nodes. It is seen that the proposed scheme yields better results in terms of Packet Delivery Ratio (PDR), end-to-end delay, throughput and routing overhead.

MultiCloud plays vital role in providing the heterogeneous types of resources to the user on-demand with minimum cost and time. Resource management and scheduling act as an influential aspect in the improvement of the performance of the MultiCloud environment. Scheduling deeds a considerable challenge in the distributed heterogeneous multiple cloud systems. The existing min-min algorithm is suitable for smaller number of tasks. The tasks are allocated to the VMs with high-processing speed. The proposed cost-based meta-task scheduling algorithm (CBMTSA) is feasible for the passive autonomous task-based scheduling for MultiCloud systems. Scheduling and rescheduling are two stages involved in this algorithm. Scheduling stage is used to allocate the tasks to the high-speed VMs. Makespan value is computed in the scheduling stage. The computed makespan is an outset value for rescheduling the tasks. The rescheduling stage is used to reschedule the tasks from high-speed VMs to low-speed VMs. The CBMTSA surpasses the existing min-min algorithm. Makespan, execution cost, and cloud server utilization ratio are the metrics considered in this algorithm.

One of the biggest challenges in today’s world in the field of robotics is rescue robotics. This paper aims in the design and implementation of mobile robot for the search and rescue operations in natural calamities such as earthquakes. These rescue robots reduce the response time as compared to humans and help in getting information to the rescue teams using sensors. The main issues concerned with the present rescue robots are modularity, mobility, durability, and robustness. The robot is designed considering all the required parameters in SOLIDWORKS CAD and simulated in Rviz with the control interface as Robot Operating System (ROS). The robot is designed in such a way that it can do all the mobility tasks like climbing stairs, moving on uneven terrains, step fields, sand, and gravel, as well as exploring tasks like finding the injured victims and hazardous signs.

Air pollution is a primary concern for humankind, causing asthma attacks, wheezing, shortness in breath, etc. In the Indian subcontinent, air quality decreases, increasing smog due to the burning of crops to prepare for the harvest and emissions from the vehicles, etc. Driving becomes difficult due to this blinding smog. To overcome these problems, a wireless network of sensors is proposed along the expressways and in public places to measure the smog. A personal area network (PAN) network of motes is set up. Using MQ135 and DSM501a sensors, smog pollutants are measured. Contiki OS and Cooja simulator are used to analyze the performance of network by emulating nodes using Tmote Sky. The nodes in this PAN are mounted over the lamp posts on the bridge to collect information of various pollutants and send the acquired data to a mote. This mote acts as a central node in the wireless sensor network (WSN) and transmits data over user datagram protocol (UDP). Finally, the parameters with warning messages are displayed at the entrance of bridges for the drivers to create a safety alert. The future aspect of this project may lead to displaying the messages at short intervals along the whole bridge with updated values. Also, the recorded data can be stored on the server and after a certain period of time, the data can be analyzed to observe the number of pollutants being emitted, and measures can be taken accordingly.

In the current global scenario, the prime question in every woman’s mind is about her safety and security. The only thought haunting every women is when they will be able to move freely on the streets even in odd hours without worrying about their security. This work suggests a new perspective to use technology to protect women. The wearable system resembles a normal watch with a button. Women can press the button when they feel discomfort and activate the system. The system can also be activated by changes in sensor setup output which is part of the system. When activated, the system tracks the location of the woman using Global Positioning System (GPS) sensor and sends an emergency email to the person who can help or save her. The system also incorporates a screaming alarm that uses real-time clock, to call out for help. The main advantage of this system is that the user does not require a smartphone unlike other applications that have been developed earlier. The use of sophisticated components ensures accuracy of the system and makes it reliable. Uneven terrains, step fields, sand and gravel, as well as exploring tasks like finding the injured victims and hazardous signs.

Wearable antennas used for various applications such as telemedicine, firefighting, and navigation purpose are integrated into fabrics. The antenna performance is described with the integration of Electromagnetic Band Gap (EBG) structure in which the micro-strip patch antenna (MSP) consists of flexible substrate material. The main goal of using EBG structure in micro-strip patch antenna is to overcome the limitations of patch antenna and to achieve better gain and efficiency, lower side lobes and back lobes level, better isolations among array elements, and by suppressing surface wave modes. The design of micro-strip patch antenna is proposed to resonate the antenna at 2.45 GHz using Jeans as substrate material for wearable applications which supports Industrial, Scientific and Medical (ISM) applications. The various characteristics of antenna such as return loss and VSWR are analyzed. The simulation of antenna is done by CST studio software. Internet of Things (IoT) may be used for the development of antennas, which supports multi-standard services within a single design.

Current trend in technology has, in turn, necessitated the amelioration in automation and the ease of performing a task. Robotic arms are playing a decisive role in all aspects of human life irrespective of the application. Not only designing but also understanding them is strenuous. This paper discusses designing and implementation of a transparent 3-degree of freedom (DOF) arm which can be analyzed and controlled to perform divergent dexterity tasks with simple commands in a plain sailing way. The arm illustrated in this paper is mounted on a mobile robot and analyzed performing varied dexterity tasks with motley complexity. A lucid graphical user interface (GUI) is developed using Qt-designer to generate commands to control this arm. In discordance to heavy industrial or complex robotic arms, it can be shifted between places and can be mounted on any platform depending on the application.

The original version of the book was published with incorrect corresponding author of Chapter “A Semi-supervised Approach to Detect Malicious Nodes in OBS Network Dataset Using Gaussian Mixture Model” “Muhammad Kamrul Hossain Patwary” (informal name) has been corrected to “Md. Kamrul Hossain” (official name). The chapter and book have been updated with the changes.