Mobile Radio Communications and 5G Networks

Intelligent transportation systems (ITSs) play a major role to manage traffic in cities. This is used to keep control and manage traffic and re-route traffic based on different parameters which has been discussed in this paper in a detailed manner. Vehicular ad hoc network is one way of implementation of ITS and mostly used setting to manage traffic and progressing quickly with time. Individuals are doing research these days for the most part in the field of media transmission. VANET is the most developing exploration region in remote correspondence. Most VANET applications are based upon the information push correspondence model, where data is scattered to a lot of vehicles. The decent variety of the VANET applications and their potential correspondence conventions needs a precise writing review. In perspective on previously mentioned, in this paper, we have contemplated and examined the attributes and difficulties of different research works identified with the applications, conventions and security in VANET. In addition to the subsequent current works, this paper is concerned about to explore different issues related to VANET. The conceivable work found the advantages and disadvantages for the future research. At last, an unthinkable examination of the considerable number of conventions is given.

IoT has become very imperative and significant since it can be incorporated to almost everything nowadays such as smart cities, smart agriculture, smart homes, and so on. Microstrip patch antenna (MPA) is very easy to build and has low production cost which makes it a very good choice in large number of applications. These applications include wireless LAN, mobile satellite communication, and global system for mobile communication, missile, and so on. All these applications owe to certain advantages of microstrip patch antenna such as low profile, low cost, low mass, and very easy to integrate. The resonance of these antennas can be achieved at any frequency by varying various antenna parameters and shape of the patch. The antennas having different shapes of patches have been discussed in this paper. As the technology is growing at a very fast rate, the application of antenna in various fields plays a very crucial role. The antenna has widespread types, and each type has a particular type of application. Power consumption is reduced with the use of microstrip patch antenna. The specific IoT application defines the design and choice of antenna which depends upon various frequency bands and the transmission strength. The paper highlights various antennas which are used very frequently in the applications of IoT along with their frequency bands.

We have studied nonclassical effects in pump mode in seven-wave mixing nonlinear process involving pump photons of different frequencies. The nonclassicality of a system increases with increase in the number of photons present in the system prior to interaction in nonlinear medium. We have found squeezing and sub-Poissonian photon statistics that can be used in obtaining noise reduction in signal processing. Further, we have also obtained antibunching of photons that can be used as a criteria of obtaining single-photon source that is primary requirement for quantum information processing.

Various forms of wireless communications technologies have been proposed for intelligent transportation systems (ITSs). Recent events have illustrated that visible light communication (VLC) can play a significant role in achieving Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Since it is energy efficient and available in abundance, it is in huge demand. Our prime objective of this experiment is to study the challenges that come during channel modulation and find optimal solutions to curb them. In this paper, we have done a comparative study of field of view (FOV) angles in different environmental conditions to evaluate the change in the signal-to-noise ratio (SNR). We performed a stress test on a previously laid model using NS 3.25 network simulator. This analytical approach helped us to successfully simulate SNR for different topological schemes and infer a hybrid model from it. Each topology scheme depicts a specific traffic scenario on the road.

The autonomous weapons which can identify the correct targets without any human intervention are in demand with the development of defense and war scenarios. The dynamics of the flight path is decided by the missile guidance system to achieve different types of mission objectives. Image processing equipped with intelligent sensors can identify any type of target other than traditional method of detection of only fire (aircrafts) or other signatures. The guidance system through image processing can differentiate between targets and can provide the latest error correction in the flight path. For detecting a particular object within an image, detection using point feature method is much effective technique. The point feature matching is done by comparing various correspondence points of object and analyzing the points between cluttered scene images to find a required object of interest in image. An algorithm which works on finding correspondence points between a target and reference images and detecting a particular object (target) is proposed in this paper. Tracking of object and estimation of motion model is also proposed by taking constant velocity and constant turn rate model. The real performance can be achieved by identifying the target in image using this detection approach and estimation of its motion.

The identification of community arrangement has taken a massive attention amid the investigators in the last few years who are concentrating on the characteristics of big complex graphs, e.g., biochemical systems, social networks, e-mail systems, the Internet, and food networks. In this paper, an attempt is made to recognize community structure employing community discovery algorithm having different purposes that identify different clusters employing node centrality. Several different clustering techniques can identify clusters of vertices termed as communities in real graphs that uncover the characteristics and hence structure of different communities. The suggested algorithm uncovers the big communities with high quality. The concept of node centrality is easier to employ to uncover best partitions and hence results in the best convergence of the aforesaid algorithm. Different investigations on actual world social networks exhibit the efficacy of the suggested technique.

In the era of the revolutionized world with a huge enhancement in the technology, various interconnecting devices are able to communicate in order to automate the today tasks for us. These IoT nodes require security, reliability, robustness and efficient management. This abundant information that shares in the network requires security measures. Blockchain technology has revolutionary impact on the world by transforming the way for sharing the information and provides a distributed environment with no centralized authority support in various industries. This paper emphasizes on the challenges faced in the integration of IoT and blockchain, surveys the various applications to analyse the potential of blockchain in the upgradation of IoT and addresses the development of some privacy preservation techniques for IoT system operating over blockchain infrastructure. This paper reflects the comparative analysis of various smart contract mechanisms used for the IoT platforms like Iota, Iotex and Iotchain. In this, we proposed the methodologies of integration used for the integration of both the technologies for the performance enhancement with respect to various parameters.

Recommendation system is a system that attempts to predict the outcome of user according to his/her interest. Recommendation system mostly uses collaborative filtering algorithms. Although this recommendation system is successful in e-commerce sites, these collaborative filtering-based recommendation systems are exposed to shilling attacks. In this, attackers insert false profile information to have an impact on prediction or recommendation of the recommendation system. The consequence of shilling attack on recommendation system, categorization of shilling attacks, detection algorithms and evaluation metrics are provided by this paper.

LEACH is the hierarchal and distributed clustering protocol of wireless sensor networks (WSNs) used to divide the network into clusters. There has been extensive research done in LEACH protocol over the past few years to overcome its limitations. This paper presents the survey of improvements in LEACH that has been done over the years and also discusses the advantages and disadvantages of all improvements.

Cervical malignancy can be viably counteracted if identified in the pre-cancerous stage. In order to appropriately treat cervical cancer, making an accurate determination of a patient’s cervical type is critical. However, doing so can be difficult, even for trained healthcare providers, because of the thin-line difference among the various cervix types. Kaggle and Mobile ODT have distributed a gathering of a few thousand commented on photographs of cervices. In this paper, we utilize profound learning approaches in computer vision, for example, convolutional neural networks and transfer learning. We try different things to experiment the models, for example, batch normalization, image augmentation, and dataset methodologies, for example, cropping the images. The initiations are utilized for the preprepared model Inception v3 which was prepared on the ImageNet dataset of 1.2 million pictures.

Majority of Indian population depends on agribusiness for its survival, and it plays a vital role in every nation’s economy. The disease is spread to other plants. Early detection of disease is a significant thing. Detection models to detect the disease are built by direct observation of every plant. This is essential as we can take parameters to restrict. Hence, healthy cropping is necessary for the growing agricultural economy. A better yield of crop is dependent on many factors including disease detection. The on time disease detection helps the farmers to save their crop yield as the remedies can be given on time. In order to solve the problem, various convolution neural network architectures have been designed and tested on labelled data to obtain high accuracy in classification and detection of disease. This work deals with the brief and detailed study of various techniques used for classification and detection of disease in plants based on feature extraction and different training methods.

In twenty-first-century virtual assistant is playing a very crucial role in day to day activities of human. According to the survey report of Clutch in 2019, 27% of the people are using the AI-powered virtual assistant such as: Google Assistant, Amazon Alexa, Cortana, Apple Siri, etc., for performing a simple task, people are using virtual assistant designed with natural language processing. In this research paper, we have studied and analyzed the working model and the efficiency of different virtual assistants available in the market. We also designed an intelligent virtual assistant that could be integrated with Google virtual services and work with the Google virtual assistant interface. A comparative analysis of the traffic and message communication with length of conversation for approximately three days is taken as input to calculate the efficiency of the designed virtual assistant.

Secure routing of information is one of the major concerns of specialist working in the field of wireless sensor networks (WSNs) as sensor nodes gather information from the physical world and transmit to base stations for authorized users to access via the internet. In a WSN, sensor nodes are deployed most of the times in unattended locations where there is the possibility of authorized or unauthorized access. This and others have brought the need to secure transmitted information from intruders. Though there are many ways to provide security to a network, cryptography has been identified as the best way for these main three security requirements which include: data confidentiality, authentication, and integrity (CIA). In this paper, we present an overview of the present challenges in a WSN and outline a number of countermeasures to solve these challenges. To tackle the challenge of security encountered in existing WSN systems, we propose an efficient and robust encryption and decryption algorithms for secure communication. The major key feature of this algorithm is that it combines both substitution and transposition cipher techniques in order to achieve the encrypted text. The algorithm is the outcome of the inspection of various existing algorithms in this field.

The exponential growth of high-speed broadband services, an increase in the number of end-users, high bandwidth, and massive data rate demand innovative and emerging points to a multipoint network that satisfies the need for next-generation passive optical access network (NG-PON). Next-generation passive optical access network stage 2 (NG-PON2)-based time and wavelength division multiplexing passive optical network (TWDM-PON) are mostly considered as the most promising optical access candidate due to its high-speed services, low power consumption, back-to-back compatibility, and cost-effectiveness. In this paper, the eight wavelengths-based bidirectional 80/20 Gbps TWDM-PON system using return to zero (RZ), non-return to zero (NRZ), and carrier suppressed return to zero (CSRZ) data modulation formats have been proposed and analyzed by varying input power, transmission distance, and bit rate. Further, the system is evaluated in terms of minimum bit error rate (BER), quality factor (Q factor), received optical power (dBm), eye diagrams, and eye height. It has been observed that the most appropriate modulation format for downstream transmission is NRZ while for upstream transmission, the most suitable modulation formats are RZ and NRZ.

The higher educational institutions are working in a rapidly increasingly complicated and competitive scenario. The contemporary challenges faced by the higher educational institutions in India are discussed in this paper. Big Data is a trending area of research that is using data analysis to notify decisions. Research on Big Data is mainly focused on evaluating real-time, compiled data and corresponds to heavy datasets to find repeating behavioral patterns instead of creating metadata of the status quo. This paper outlined the effect of Big Data analytics to improve faculty members’ understanding of learners’ perspectives and behavior toward the programs as well as opportunities and challenges associated with its knowledge and implementation.

In this paper, a congestion control TDMA and FDMA-based MAC mechanism for optimizing data flow in a tree switched network is suggested. A method has been devised for minimizing the communication loss and delay for carrying heavy network traffic from data centers to fixed data points. A two-stage approach is adopted, where traffic analysis is done first and then node prioritization is made considering different quality parameters. Node’s individual contributions are calculated and based on their contribution’s frequency allocation using FDMA will be applied. Later, load on critical cross-points will be observed and TDMA scheduling method is devised. Simulations are done in NS2 environment. This method not only reduces communication losses but also improves throughput.

Detection and classification of objects within images are an actively pursued field in computer vision. For more than a decade, a lot of work has been explored in the maritime surveillance domain but research in computer vision for maritime is still in the nascent support and several challenges remain open in this field. Although the tracking of ships widely explored, object detection of non-ship objects remains an unexplored part. The present work describes an automated novel method to detect different ship and non-ship objects. The proposed work is based on support vector machine (SVM), to train the algorithm with large data set. We have used the canny filter to detect the edges of an object and further extracted the features of images using bag of features. The algorithm is trained with the large set of extracted features of objects. Experimental results show the detected objects along with its classifications. The algorithm is implemented on MATLAB environment.

With the development of various image editing tools and techniques, the forgery has become a common aspect in the image domain, nowadays. We can now insert, delete, or transform a small part of an image very easily. We can also copy-paste an image into some other image or the same image frequently. The image forgery mainly focuses on passive-based approach, because it does not involve the addition of any information into the image unlike active-based approach. Our proposed method mainly focuses on copy-move forgery and splicing forgery detection which are types of passive authentication techniques. The proposed method uses four convolution and pooling layers in a succession with different parameters. A filter is applied on each convolution layer, and all the layers are initialized with some weights and a bias. Using a hidden layer after four convolutions and pooling layer and finally using output layer for decision making with a result of 86.4% accuracy, precision of 81%, recall of 79%, and F-measure of 84% as compared with state-of-the-art approaches.

Everyone is aware that existing approaches to IoT networks have nothing to do with certain security issues that may expose sensitive data to unauthorised user. Today, low-power and lossy networks (LLNs) speak as one of the most fascinating examination regions. They incorporate wireless personal area networks (WPANs), low-power line communication (PLC) systems and wireless sensor networks (WSNs). Such systems are frequently improved to spare vitality, bolster track designs not the same as the standard unicast correspondence, run steering conventions over connection layers with limited edge sizes and numerous others. The IoT is a quickly developing innovation. In IoT, the gadgets (device) are associated through the Internet and controlled from any remote territories. Before the approach of IoT, the association between the clients was distinctly through the Web. By 2020, there will be 75.4 billion gadgets interconnected through the Web. In IoT, we have routing protocol for low-power and lossy networks (RPL). RPL is a lightweight convention that has decent directing usefulness and mindful setting, and it underpins dynamic topology, having fundamental security usefulness. This paper explores the power-efficient and secure ipv6 routing protocol RPL and proposed the Caesar Cipher hash algorithm for the trust privacy and security of the information of IoT node with power full tool CONTIKI COOJA SIMULATOR security solution (CCSS).

Human can grasp and release any object based upon the tactile feedback. Without the feedback, the ability for fine control of a prosthesis is limited in the upper limb amputees. Based on the discrete event-driven sensory feedback control (DESC), there is a device that informs the users on the completion of the discrete events such as object contact and release in the form of vibrotactile feedback. The device (DESC-glove) comprises sensorized thimbles to be placed on the prosthesis digits to sense the contact events, a battery-powered electronic board, and vibrating units embedded in an arm-cuff. In this paper, we have presented the sensitivity in terms of the force applied on the thimble at different positions with different force rate. An experimental setup was designed in order to characterize the two thimbles with different sensors. The sensitivity was 0.82 ± 0.16 and 1.18 ± 0.05 N for two thimbles. The main motivation to present the sensitivity of the thimble in different position was to overcome the limitation of the previous design.

2,4-Dihydroxyacetophenone benzoylhydrazone (DHABH) collectively has been used for the anticorrosive activity towards soft cast steel and as a complexing agent for formation of a coordination complex with tungsten (VI). The corrosion inhibition properties of the reagent are examined in acidic medium at variable concentrations and temperatures utilizing different techniques like gravimetric and electrochemical including polarization measurements and electrochemical impedance spectroscopy (EIS) and quantum chemical calculations. It has been found from the study that inhibition potency increased with increasing concentration of DHABH as indicated by weight loss measurement, polarization curves and EIS studies. However, the complexation conduct of the reagent with tungsten (VI) to form 1:3; W(VI): DHABH complex is studied spectrophotometrically. The produced method of determination has been observed to be exceedingly sensitive, selective, quick, reproducible and satisfactorily pertinent to a wide variety of technical and synthetic samples.

Sentiment analysis (SA) is based on natural language processing (NLP) techniques used to extract the user’s feelings and opinions about any manufactured goods or services provided. Opinion mining is the other name for sentiment analysis. Sentiment analysis is very useful in the decision-making process. With greater Internet use, SA is a powerful tool for studying the opinions of customers about any product or services provided by any business organization or a company. Several approaches and techniques have came to existence in past years for sentiment analysis. Sentiment analysis is useful in decision making. In this paper, we offer an exhaustive description about techniques used for SA, approaches used for SA and applications of sentiment analysis.

For long, software-defined network (SDN) has been the new trend in the field of networking. Despite being way better than the traditional networking approach, security issues related to SDN are a major concern for technologists all over the globe. It has drawn great attention and is still a highly interesting field of study for network administrators. Among the various means suggested to overcome these security issues, working with securing Border Gateway Protocol (BGP) seems to be a feasible solution to securing SDN. BGP deals with transferring data between two autonomous systems by exchanging routing information between the systems. In this paper, we propose an efficient way to secure data transfer, through BGP, between two autonomous systems in SDN. This is achieved by encrypting data using Advanced Encryption Standard (AES) Encryption Algorithm. In order to prove its feasibility, AES data encryption algorithm is implemented on two hosts in SDN and data transfer is traced on Wireshark. Furthermore, we extend our study to conclude that SDN can be secured by encrypting data over BGP and then transferring it safely to the destination.

In modern time, emotions affect people in many aspects of life. Long-term emotional problems lead to mental and physical problems such as depression. Wrist pulse contains the information regarding the physiological and pathological state of an individual. Overall mental and physical status of human can be checked with the wrist pulse through pulse examination or nadi parikshan. This paper discusses the parameters of wrist pulse in terms of Vata, Pitta, and Kapha energies for different emotions (anger, calm, fear). Manually, Vata, Pitta, and Kapha are sensed by the medical practitioners through three fingers. Here the pulse data is being acquired through the optical pulse sensor interfaced with the ATMEGA328 microcontroller. Signal processing techniques as approximate entropy, sample entropy, augmentation index, pulse rate are applied in MATLAB to extract the features of Vata, Pitta, and Kapha to recognize the emotions. It is found that Vata rate is high for fear emotion while Pitta rate is high for anger emotion and for calmness emotion, Kapha rate is high. Approximate entropy, sample entropy, augmentation index are more for anger emotion than other emotions.

Micro determination of Zirconium (IV) has been carried out using 3-Hydroxy-2-[2′-(5′-methylthienyl)]-4H-chromen-4-one (HMTC) as an analytical reagent. Zr (IV) forms a 1:4 (M:L) yellow coloured complex with HMTC extracted into dichloromethane from ammoniacal medium (pH 7.05–7.09). The complex system shows a maximum at 424–440 nm and follows Beer’s law in the range 0.0–0.9 µg Zr (IV) ml−1 with an optimum range of determination as 0.27–0.79 µg Zr (IV) ml−1as detected from Ringbom plot. Zr (IV)-HMTC complex has molar absorptivity of 8.22 × 104 Lmol−1 cm−1, specific absorptivity of 0.900 ml g−1 cm−1 and Sandell’s sensitivity value 0.0011 µg Zr (IV)cm−2; the linear regression equation being Y = 0.981X − 0.036 (Y = absorbance, X = µg Zr (IV) ml−1) with the correlation coefficient 0.9987. Detection limit of the procedure is 0.0174 µg ml−1. The repercussions obtained are highly consistent with the standard deviation of ±0.0039 absorbance unit and has been confirmed by student’s t-test with 0.5% limit. The proposed technique has been successfully applied in diverse synthetic and industrial samples.

Semantic block detection is an approach to deal with mining of data from Web pages and Web applications. Conventional strategies cannot perform better, as the new Web site configuration develops with new technologies. Extraction of Web information from the full Web page will be the intensive assignment to recover the substantial info because they are Web site programming language subordinate. A “layer tree” is developed to replace various levels of irregularities between the DOM tree portrayal and the visual format of the Web page. There are various limitations and barriers arise for the different scenarios and to find out and resolve the issues accordingly with the degree of complexity of the problem. As the flow of information has been upgraded and updated on the Web pages exponentially on the daily bases and to find out the correct information in this bulk of data is the real task. VBPS helps in finding the deep data among the huge Web pages but as the technology has been updated dynamically this approach has faced with some limitations. In this paper, we are discussing about these limitations and its comparison with the other techniques in this field.

In this paper, the trends and the timings of stock market of Japanese Candlestick are predicted and analyzed empirically by developing the hybrid model that uses the three prominent techniques of artificial intelligence, i.e., SVM, ICA and genetic algorithms. In order to conduct the effective technical analysis of stock market—support vector machines (SVM) are used with genetic algorithms and imperialist competition algorithms (ICA). ICA is used to indicate the stock market timing and to optimize the SVM parameters, whereas GA is used to select the best features in addition to SVM parameters optimization. The input data of a model is generated using the two very important approaches—raw-based and signal-based. The results of the paper indicate that the performance of SVM-IC-GA is far better than the existing feed forward static neural network techniques of the existing literature.

The use of technology in the field of medical sciences has increased a lot since the last decade. Nowadays, you can see many technologies like computers and cameras used in medical sciences. They not only help in detecting the disease or the cause but also help in the curing process by maintaining records. The use of computers for image processing has provided an upper hand to the physicians. By using this technique, anybody can tell if there is a fracture in the bone or not. Fractures are common these days, so fracture detection is a crucial part of orthopedic X-ray image analysis. The automatic fracture detection technique helps the doctor to start medical care immediately. We propose a new technique using machine learning and digital geometry. The method can detect bone fractures by bone contours by removing discontinuity processed by segmentation. It overcomes the shortcoming of the previous method which only works on texture analysis. In this, several digital X-ray images are taken as input, and the machine will give the output whether there is a fracture or not based on particular algorithms.

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. In this paper, the focus is to enhance the intelligence of the microgrid network using a multi-agent system and validation is using network performance metrics such as delay, throughput and jitter. Network performance is analyzed for the medium-scale microgrid using two IEEE test systems, i.e., IEEE 34 and IEEE 39. In this paper, Bellman–Ford algorithm is incorporated to calculate the shortest path to a given destination. The algorithm is defined for the distributed nature of the microgrid. From this model, researchers have achieved up to 30% improvement in the network performance of a microgrid.

The denoised signal with enhanced quality is utilized for correct analysis of biomedical signal. In real-time applications, these signals are mostly affected by distinct types of artefacts. The objective of this research paper is to design the optimum digital filter for the elimination of noises lies in high- and low-frequency bands. The proposed methodology is implemented using approximations and windowing techniques. The results of frequency responses of both the designed filters are evaluated and compared on the basis of pole–zero plot and gain. It is interpreted from the simulated results that the design technique with the help of Kaiser window results in −61.75 and −56.89 dB for high-frequency and low-frequency signals, respectively, while assuming shape parameter as 0.5. The proposed design method can be used in different digital signal processing applications.

Millimeter wave technology is the solution of the current generation of the mobile users to provide uninterrupted signaling and high data rate. In this paper, a compact 1 × 6 antenna array is designed at 28 GHz millimeter wave frequency application with 2:1 VSWR. For the impedance matching, inset feed is utilized with the circular patch. The proposed antenna occupies 37.60 × 8.45 mm2 space on the Rogers RT/duroid 5880 dielectric substrate. The designed array antenna achieves 27.654–28.291 GHz bandwidth, 12 dBi gain, 88.04% radiation efficiency, at the resonant frequency.

In the earlier few years, more concern has been focused on wireless sensor networks with its large range of applications in different areas even in critical situations like commercial applications, battlegrounds, pollution sensing, habitat observing of animals, smart buildings and homes, secure homeland, military monitoring, hospitals, and other various locations; thus, there are many chances of attacks in WSNs. Security is major concern to control of these attacks for a secure data as well as secure transmission, in wireless sensor network. Our main focus in this work is to provide a detailed comparison of various attackers site-based attack and access level attacks on various network layers of WSNs.

This paper aims to design and execute the advanced development in home automation system based on non-conventional energy source for conserving energy. Nowadays, people are too engaged in their busy schedule and are unable to switch off the lights when not in use. The present system is like: the lights will get turn ON/OFF only when person entering/exiting the room presses the switch manually. Also, the inverter battery gets charged from the main AC supply, which further leads to more energy consumption. This paper gives the best solution to reduce high energy consumption by appliances. Also, the manual handling of the lighting system and charging of inverter battery through the main AC supply are completely eliminated. The main purpose of this paper is to provide continuous power supply to an appliance, by selecting the supply from any of the source, namely DC battery charged through solar panel and main AC supply automatically, in case if one of the sources is absent, and this is done with the help of microcontroller. Finally, the objectives of the prototype, which are to be implemented, have successfully achieved.

Cloud computing is a computing software that enables a user to manipulate, configure, and provide access to the applications over the network. It is a model that allows the user to take advantage of computing facilities over the web as per user demand and to share resources such as network, data storage, servers, and applications without the need of actually installing it on their device. Thus, it can be said that it is a computing model that helps manage cost and saves time. Cloud computing has its applications over many fields like health, education or banking due to its special features. Cloud computing is an Internet-based software, and thus, it is the responsibility of cloud service providers (CSPs) to maintain data stored by users at data centers. Scheduling in cloud plays a very important role to achieve maximum utilization, and user satisfaction resources need to be allocated effectively. In this paper, we have discussed some optimum scheduling technique to enhance the performance of cloud. We consider cost as one of the major attributes that result in enhancing the performance (Cloud computing tutorial tutorials point), (Armbrust et al. in Commun ACM 53:50–58, 2010).

Data aggregation plays an important role over WSN as aggregated data is utilized for decision making/analysis purpose; but due to complex aggregation computations, sensors may consume excessive energy and thus may reduce the network lifespan. So there is requirement to optimize the aggregation process. In this paper, an optimized data aggregation scheme, called optimized multi-level data aggregation scheme (OMDA), is introduced using LEACH protocol. Its performance is analyzed using different performance parameters (throughput/end-to-end delay/energy consumption/network lifespan) under the constraints of sensor node density that varies from 50 to 200.

Biosensors have been the most alluring research area since long times back due to their highly correlation with human beings, health issues and environment. Constraints in the way of fabrication and design of biosensors for commercial applications require constant attention. Certain materials can exhibit different properties based on its shape and size and have been realized with the advent of an interdisciplinary and integrated present-day science profoundly called as “Nanotechnology.” Intervention of nanotechnology in this biosensor field provides some exceptional electronic, optical and biological properties of these nanomaterials that find their use in variety of applications like glucose monitoring, estimation of harmful diseases, hazardous chemicals detection and drug discovery. Nanomaterials have empowered lower working potentials and facilitate electron transfer and lower detection limits, thereby improving the sensing capacity of the device. This paper mainly highlights the concept of biological sensors, their applications and different ranges of nanomaterials used in conjunction with the biosensor field to get a faster and reliable detection of electrical signals. Reducing dimensions in nano range opens up various opportunities in the biosensor field for significant improvement in their characteristic parameters.

In this paper, a fifth-generation microstrip patch antenna has proposed. The proposed antenna design is working on 10.04 GHz with a return loss of −56.65 dB. The patch antenna has a compact structure of 18 mm × 18 mm with a FR4 glass epoxy substrate of 1.6 mm thickness. The results are simulated using Computer Simulation Technology Microwave Studio.

The attractive characteristics of the cloud computing environment encourage its growth and penetration in various sections of society like government, education, entertainment, etc. The large-scale adoption of cloud computing not only provides services to users but also presents a wide attack landscape to the attackers and intruders in order to perform sophisticated attacks. Widespread implementation of cloud computing and its distributed and decentralized existence makes this computing paradigm prone to intrusion and attacks. Thus, the creation of network intrusion detection framework using anomaly detection method for cloud computing network with a better assault identification level and less false positives is essential. This paper discusses an effective network-based intrusion detection model utilizing artificial neural network strategies such as multilayer perceptron programmed with a genetic algorithm, as well as compares it using other machine learning techniques. The genetic algorithm was incorporated in multiple layer perceptron to predict the connection weights. Standard IDS dataset, namely CICIDS 2017, was used for simulation and testing of the suggested model. The results of implementation demonstrate the ability of the proposed model in the identification of intrusions in the cloud environment with a higher rate of detection and generation of minimal false alarm warnings, which suggests its dominance relative to state-of-the-art approaches. The implementation results show an accuracy of 90%.

VANETs stands for vehicular ad hoc networks. In VANETs, alerts like post-crash notification (PCN), beacon messages, etc., (with sender id, position, speed and timestamp) are exchanged between vehicles in order to improve road safety so that the driver is previously alerted of the hazard or crash that she/he could face ahead. This technology has a great potential to reduce the number of accidents that are happening every year. If the driver is alerted few seconds before the accident about the hazard, then the accident could be prevented from happening. But, in VANETs, there is a possibility that due to selfish or malicious reasons, some attacker might send false alerts and falsified information in beacon leading to change in driver’s behavior and entire network. This could result in accidents in the network or long-distance travel of driver. Hence, it is very much necessary to detect the false messages that are communicated in vehicular network.

Artificial intelligence (AI) is defined as the power of intellectual human mind that designs an intelligent working system which proves in terms of computational power designed by the human intelligence. AI is designed in such a way that it commences to simulate the thought of brain, their thinking pattern, analysing approach and way of computing the problem. AI is one of the most notable fields in the current scenario of Fourth Industrial Revolution. AI is not a concept which finds its application in single field, but it can be used in many domains such as health care, medicines, evolutionary computation, security purposes, diagnosis and evaluation, image classification, accounting databases, transportation and smart cities. AI explores new routes of computation and follows heuristic approaches in order to solve biological problems. The present paper defines applications and current role of AI in health care and how it relates in studies including diagnosis process, image classification for diagnostic sciences, measuring the tendency of congestive heart failure, genetic analysis, drug discovery and much more. New techniques and methods are always a great tool to learn more, analyse more, as well as extract some useful information. Finally, a broad perception of this emerging topic is mentioned here to prove the positive role of AI with biotechnology and health care.

Breast cancer is the utmost frequently occurring as well as the most common reason for cancer-related deaths among women community worldwide. In Indian females, breast cancer ranks with the highest rate as 25.8 out of 100,000 with the mortality rate of 12.7 per 100,000 women. Early detection and accurate diagnose will facilitate the clinicians to fight against this deadly disease worldwide. To differentiate between the patients at higher risk and lower risk of breast cancer, various risk factors and risk analysis models have been developed. Machine learning-based models help in the categorization of high-risk and low-risk patients. Once categorized properly, high-risk patients require more surveillance, prophylactic count, and other preventive measures like chemoprevention or surgery. Patients with low risk should also be kept under surveillance to minimize the probability to turn in high-risk patients. In this paper, the authors have identified the key risk factors for breast cancer. The authors have done a systematic review of different risk assessment models for breast cancer.

The aim of the present investigation is to study the propagation of plane harmonic waves in transversely isotropic homogeneous magneto-thermoelastic rotating medium with multi-dual-phase lag heat transfer. In the current research, it is observed that there are three types of coupled longitudinal waves (quasi-longitudinal, quasi-transverse and quasi-thermal) for the 2D assumed model. Different thermoelasticity theories (coupled theory (CTE), Lord–Şhulman (L–S) theory, Green–Naghdi (G-N) theory and multi-dual-phase lag (MDPL) theory) are used to study the propagation of plane harmonic waves. The characteristics of various reflected waves such as amplitude ratios, energy ratios, phase velocities, specific loss, penetration depth and attenuation coefficients are computed and depicted graphically. The conservation of energy at the free surface is verified. The characters of wave with different theories of thermoelasticity are represented graphically.

The present research deals with the study of deformation in generalized transversely isotropic magneto-thermoelastic solid with two temperature (2T), rotation, due to inclined load and laser pulse. Generalized thermoelasticity theory has been considered for this mathematical model. The entire thermoelastic medium is rotating with uniform angular velocity and subjected to thermally insulated and isothermal boundaries. The inclined load is supposed to be a linear combination of a normal load and a tangential load. The Fourier and Laplace transform techniques have been used to find the solution to the problem. The displacement components, conductive temperature distribution and stress components with the horizontal distance are computed in the transformed domain and further calculated in the physical domain using numerical inversion techniques. The effect of laser pulse in different theories of thermoelasticity is depicted graphically on the resulting quantities.

This paper investigates the impact of integration of wind turbine on the dynamic performance of system. The power system is integrated with intermittent source, i.e. wind power plant. The unpredictable nature of intermittent sources always distracts the system stability. In this study, the integrated wind turbine impacts the power grid. Integration of these intermittent sources deflects the frequency from its reference and requires a controller to keep the system frequency within limits. In this study, a comparison is performed between classical and intelligent controller by using simulation done in MATLAB simpower system toolkit.

The era of technology is going to be changed so frequently, data size keeps on increasing exponentially, and as the data is increasing day by day, many new things are coming up in front that has to be considered in while getting information from the dataset. One of the most popular algorithms based on frequent itemsets is the Apriori algorithm. As the data size is going to be increased every day, number of items is also increasing, in such cases, the Apriori is not able to provide the best solutions, in such cases, the solution comes like the algorithm should run parallel, but it is not feasible solution. The solution can be feasible if the right method is hybrid with the Apriori. The MapReduce is one the best approaches that can provide solution in an efficient manner. The Apriori is also working on the frequency of the data points, and MapReduce can also work on the mapping of the key value with frequent data points. Therefore, the MapReduce can provide the parallel solution to Apriori in an efficient manner. In this paper, a hybrid version of Apriori and MapReduce for the fast and efficient execution is shown. The Apriori algorithm deployed on the MapReduce platform with suitable frequent key values. The hybrid approach is executed on the dataset and provides more accurate result. Experimental results show that the algorithm scales up linearly with respect to dataset sizes.

Ambulatory ECG signal gets coupled with various noises. Noise and ECG signal are non-stationary in nature. Filtering system, an essential part of the ambulatory ECG system, needs to be less complex so as to minimize the overall processing cost. Present paper proposed time varying adaptive filtering system comprising of complexity reduced variable step size algorithm and a cascaded digital FIR filter. The MIT/BIH arrhythmia dataset has been used to evaluate the proposed system. Results obtained in terms of improved SNR and fast converging learning rate demonstrate that the proposed system can effectively remove noise compared with other popular adaptive filters.

The design and characterization of a compact, ultrathin, polarization-insensitive, triple-band metamaterial microwave absorber are presented. The designed absorber consists of three metallic resonators printed on the top of a 0.8-mm-thick FR4 dielectric substrate. The structure is designed to achieve triple-band absorption at 3.92 GHz (S-band), 5.92 GHz (C-Band), and 9.2 GHz (X-Band) with 92.2%, 94.5% and 98.71% absorption, respectively. The proposed design is 0.0245λ thick, and its periodicity is 0.3068λ corresponding to its highest frequency of absorption. The design is compact and ultrathin as compared to several already reported dual- and triple-band absorbers. The absorber exhibits wide angular stability up to 60° angle of incident wave. A prototype of the designed absorber has been fabricated, and the measured results are observed in agreement with the simulated ones. The compact size and ultrathin thickness make the design fit for potential RF applications such as RCS reduction etc.

In this paper, various characterizations of line-of-sight (LOS) optical wireless communication system (OWCS) based on WT-ANN are explored. To enhance the performance and to reduce the fluorescent light interference (FLI) in OWCS, the proposed system is designed and simulated using stationary wavelet transform (SWT)-based on–off keying (OOK) modulation, which has been proposed using discrete wavelet transform (DWT) in the previous study. The proposed structure enriches the performance of the OWCS, bearing in mind various parameters like reduction of noise, efficiency, data rate and overall improved performance of the scheme.

In this paper, various characterizations of the optical wireless communication system (OWCS) based on WT-ANN for PPM modulation are explored. Stationary wavelet transform (SWT) is designed and simulated in the proposed system, to enhance the performance and to reduce the fluorescent light interference (FLI), based on pulse position modulation (PPM), in OWCS, which has been proposed using discrete wavelet transform (DWT) in the previous study. Reduction of noise, improvement in efficiency and overall improved performance are the various parameters taken into consideration to enrich the performance of the proposed structure of the OWCS.

Effective deployment of IoT devices to preserve security, with minimum computations and energy consumption will be great challenge in the current world scenarios. This research work discusses the problem of deploying IoT devices and key exchange among IoT devices. These devices are prone to physical attacks, reason is their unattended deployment. Security of IoT devices is difficult to achieve, because of the heterogeneous array of servers and other devices. In this paper, labels are assigned to each device in virtual hypercube overlay to facilitate communication. Since hypercube deployment has the least computational complexity among other private key management solutions. Finally, communication between IoT devices deployed in hypercube arrangement is done using group Diffie–Hellman key agreement protocol. The proposed protocol is more efficient in terms of assigning a new labeling scheme to IoT devices (placed at hypercube nodes) and then uses these labels to generate shared secret key. This labeling scheme does in fact allow for effective key exchanging among IoT devices.

Despite the unceasing advancements in medical science and growing awareness, cancer continues to be a deadly disease that claims almost ten million lives every year. The alarming number of fatalities is caused due to privation of timely cancer detection, tardy medical attention or in some cases from patients losing the will to live due to a protracted and unending treatment procedure. Governments across the world are taking steps to ensure timely cancer detection and treatment. However, little attention is being paid to the seemingly unending treatment course taking a toll on the patient’s mental health thus crushing the patient’s spirit to continue. Through this work, we propose an approach for timely detection of depression in cancer patients by carrying out affect analysis of cancer communities on Twitter. We use the Plutchik’s wheel of emotions in psychology as a tool for depression detection. We carry out the Twitter profiling of ten persons who are part of a cancer support group on Twitter and carry out our experiments on this dataset to demonstrate the suitability of the proposed technique.

An ultrathin triple-band polarization-insensitive compact metamaterial absorber for microwave frequency applications is presented. The top layer of the proposed structure is designed with a metallic hexagonal circumscribed pattern with concentric rings. It exhibits distinct absorption peaks at 7.69, 10.56, and 15.60 GHz covering C, X, and Ku bands. The proposed absorber is ultrathin with a thickness of 0.044λ0 and compact in size with unit cell dimensions of 0.44λ0 w.r.t highest frequency of absorption. An array of the proposed structure is fabricated, and experimental results are observed in agreement with the simulated responses.

Content-based image retrieval (CBIR) is one of the most prominent systems by which the desired and relevant images are recovered from a massive database by using the basic image features like color, shape, texture, spatial information, and edge. In this paper, an experimental analysis is being done to determine the most efficient combination using color and texture descriptors. Here, color moment (CM) is used for color feature extraction and is used individually in a combination with different texture descriptors, namely discrete wavelet transform (DWT), Gabor transform, Curvelet transform, graylevel co-occurrence matrix (GLCM), and local binary pattern (LBP). These color and texture features can be combined using two different levels of the system: feature-level fusion and score-level fusion. Both the feature-level and score-level combination techniques are used in this paper. The results of this analytical experimentation depict that the framework of CM, and GLCM attains the highest results among all the other combinations using feature-level fusion technique on a benchmark CBIR dataset, particularly WANG. Precision, recall, f-score are some of the evaluation parameters which are utilized in this paper to measure the effectiveness of the fused descriptors.

In recent times, field-programmable gate array (FPGA) has done a lot of improvement in the area of IoT, a computing device that works like a microcontroller. In this segment, there is an emergence of nanobridge-FPGA that has the ability to improve the performance and minimizing the power consumption in IoT devices. It is a field-programmable gateway array that incorporates genuine nanobridge metal atom migration-type switch which is highly durable and resistive against high temperature and radiation. FPGA has already shown improvement over CPU in terms of efficiency with its group of integrated circuit that provides hardware switching using memory modules and semiconductor switches but nanobridge-FPGA enhances the power efficiency by using space-saving nanobridge switch instead of semiconductor switches and memory modules. In this work, the discussion will be on the working principle of nanobridge including the architecture of nanobridge-FPGA and its comparison with the performance of available FPGAs and CPUs.

Li-Fi implies light fidelity, a wireless data transmission technique which uses illumination or light as medium of communication. Li-Fi is an innovative technology based on visible light communication (VLC) that creates light as a media of communication replacing the cable wire communication. Li-Fi is evolved to beat the speed in Wi-Fi. The paper summarizes most of the research, developments and applications achieved so far and looks at the various aspects of the strengths and weaknesses of Li-Fi’s technology.

This paper proposed a 1 × 16 antenna array to cover 28 GHz bands for 5G wireless communications systems. The design approach is based on 16-element tapered antenna array with corporate (parallel)-series feed structure network. The individual element of the antenna consists of the inset feed for achieving the proper impedance matching and tapered patch for achieving the desired gain, bandwidth and miniaturization. The whole configuration is designed over a Rogers RT/duroid 5880 dielectric substrate size of 28 × 30 × 0.79 mm3. The measure peak gain is 15.85 dB at 28 GHz. The simulated result shows a return loss of −45.73 dB, bandwidth of 27.56–28.381 GHz, gain of 15.85 dBi and 93.36% of radiating efficiency.

Terahertz frequencies have become significant in communication systems to execute the huge demand of the next-generation wireless communication systems for high data rate, high capacity, and low latency applications. In order to achieve high-speed wireless communication, antenna designs with high gain and high radiation efficiency are needed. The present research demonstrates the microstrip curvature patch antenna design with inset feed on polyimide inhomogeneous substrate using periodic photonic band gap crystal. Photonic crystal substrate contains several sets of air holes perforated in the polyimide substrate, where each set has its own specific radius. The proposed antenna design covers frequency range from 0.6342 THz to 0.6911 THz and resonates at 0.6588 THz with −43.47 dB return loss at 2:1 VSWR. The simulated result shows that the proposed antenna design achieves 8.956 dBi gain with 86.96% radiation efficiency, 0.6342–0.6911 THz bandwidth at the resonant frequency of 0.6588 THz which makes it suitable for the THz wireless communication system.

With a mortality rate of 17.9 million per year, heart disease has emerged out to be the deadliest disease of the world. Early detection of this disease can reduce mortality. Data mining based disease diagnosis systems can aid medical professionals in the correct and timely diagnosis of the disease. In this study a Python-based data mining system, capable of diagnosing the heart disease using decision tree, KNN classifier, naive Bayes, random forest, and support vector machine (SVM) classification data mining methods, has been developed. The system was applied to four heart disease datasets obtained from the UCI machine learning repository. The relative performance of various data mining techniques was evaluated by comparing the results. The results showed that the support vector machine, with 98.7% efficiency, 98.4% precision, and 99.2% recall, has emerged out to be the best method for the diagnosis of heart disease.

The Scientists and engineers are engaged in finding the cause-and-effect relationships in various processes since time immemorial. It has helped them understand and optimize the interplay of various process parameters to achieve the best output. Just like any other scientific research, it is equally important to conduct experiments, collect data, and develop mathematical models for the welding processes. The increased automation and mechanization has made it further important. The mathematical modeling approaches used in welding research have evolved considerably with time. This paper provides an overview of various mathematical modeling approaches used in welding research. The statistical modeling has emerged out to be the best approach in present times.

Cloud computing is an integrated phenomenon that incorporates data, applications and services in a dynamic environment and enables worldwide optimization of resources. This computing technology is scalable and elastic in nature that opens door for large amount of incoming data from different venues with high velocity. Managing such data in distributive and heterogeneous environment imposes a challenge of load balancing on the service providers. They need to allocate the incoming tasks efficiently to the computing nodes to avoid imbalanced mapping and execution of the tasks. To achieve efficient load balancing, various load balancing algorithms have been proposed, and they all focus on achieving the effective distribution of data and improve the associated measurement factors. In this paper, different load balancing algorithms have been studied and analyzed with description of their techniques and focused parameters. Then, there is a brief discussion on the existing load balancing algorithms and further compares them based on parameters like throughput, scalability, resource utilization, etc., followed by the important findings thus made.

From the last few decades, wireless communication networks (mobile) have experienced a remarkable change to attain advancement for maintaining the systems QoS with data rate for multimedia streaming. Some extensions of these networks came to picture for a transformation into speed, technology, frequency, data capacity, latency, etc. with extreme precision levels. It gives the enhanced lifetime and network connectivity of the system. This visualization of the next-generation wireless networks is of various types of radio access technologies such as Advance LTE, WiMax, and Wi-Fi. All invention has some principles, diverse capacity, and a new technique with new features which make a distinction from the previous one. These all extended forms of wireless networks are based on the heterogeneity of the network. For achieving these said objectives, a QoS with optimal confederation-aware technology, i.e. QOC-RRM method, is discussed. Hence, this predictable expose gives an idea about an LTE network for future-generation radio resource management. Our proposed technique makes use of the QOC-RRM method. In this hybrid RDNN method, i.e. recurrent deep neural network, we present differentiate operators based on multiple constraints through priority-wise. This QOC-RRM method controls the due source through the sink or in some cases base stations. The consumer not at all practiced earlier than such high-value skill that includes the entire advancement features. This proposed work is implemented by using the Network Simulator (NS2) version 2.34 and its extension NS3 tool. The performance outcome shows that the proposed work outperforms as compared to the existing work, i.e. the conventional RRM scheme. The parameters used are the radio spectrum utilization, the utmost amount of dynamic user, and the least rate of the data required.

Many low-power devices mounted on a body and connected in a network form a wireless body area network (WBAN). The physiological signals generated by the body are captured by these low-power devices called as nodes and are then transmitted to the base station or sink for further processing. The sensor nodes deployed on the human body form a network and share the information by the use of different routing protocols which have a significant impact on the dissipation of energy and reliability of the networks. Sensor nodes can self arrange themselves for the configuration of cluster head in a non-centralized hierarchical routing. The nodes are unacquainted about the whole rational structure of the network during self-configuring. The base station first collects information and residual energy of each node in a planned routing technique. This paper proposes an energy-efficient reliable routing protocol for transmission of data from the nodes to the base station by cluster formation with the analysis of the residual energy of all the nodes. The comparison of the proposed protocol energy efficient and reliable routing (EERR) with that of the M-ATTEMPT protocol based on energy dissipation with time, data packet sent and the system life span of network shows that system life span is increased for the network.

Internet of things (IoT) digitalized the worldwide system containing individuals, connected things, smart devices, data, and information. It is a well-known fact that as an ever-increasing number of devices interface with the Internet, the difficulties of making sure about the information that transmitted and interchanges that they start are getting progressively significant. Throughout the years, we have seen a flood in IoT devices, comprehensively in two parts: homes and manufacturing. Since these are autonomous and secure fields, the duties of making sure about the devices rest with the platform providers. In this paper, we have discussed the various application areas where IoT is applied to get an effective and reliable outcome, and majorly, we have focused on security aspects related to IoT. For that purpose, we have proposed a security model to protect the IoT network or system from unwanted threats and attacks. The proposed model is providing a choice of a suitable security method and protocols for IoT Security layers. This model is used to improve the performance of IoT system by opting the appropriate security methods for IoT layers to reduce the power and time consumption.

Many bridges in India as well as in the world are on the edge of deteriorating, and their life span is already finished and yet they are still in use. These bridges are a risk to many lives. There are many factors which contribute in making these existing bridges dangerous which are heavy load of vehicles on a single bridge, high-level water or pressure, and heavy rains. With recent advancements in Internet of things (IOT) and other technologies integrated with wireless sensor devices, structural monitoring of bridges can be done using structural health monitoring (SHM) systems. Nowadays, wireless sensors can process real-time data and measure the parameters like displacement and hence can be useful in detecting the damage of the structure; the results are sent through a standard protocol to the servers on the Internet, i.e., cloud. In this paper, we suggest SHM damage detection technique via various sensors such as piezoelectric sensor and the usage of self -healing material Epoxy filled fiber-reinforced polymer (FRP). SHM system proposed in this paper consists of raspberry pi, analog-to-digital converter, Wi-Fi module, and various sensors to measure the various parameters of the bridge.