Architectural Wireless Networks Solutions and Security Issues

The cloud computing standards are gaining an increased research interest due to various benefits they offer. Though there are so many influences with cloud computing, security and privacy problems are various issues handling with the extensive adaption by the model. Malicious problem of service provider is one more issue which cannot be traceable by data proprietors. Hence, finding the appropriate solutions to these security issues at both administrator level and customer level is very attractive in various directions. Cryptographically enforced access control for securing electronic pathological records (CEASE) is formulated by extending the proposed ciphertext-based attribute-based encryption (CP-ABE) with advanced encryption standard (AES) through limited-shuffle techniques. The main objective of CEASE is to provide data confidentiality, and access control limited-shuffle protects the data from inference attacks and protects the data confidentiality for hot data. In the next step, this research works design a multistage encrypt-or model by differentiating the users as public and personal. Two separate algorithms such as Vigenere encryption algorithm and two-fish encryption are applied in personal and public domain, respectively. Further, where, hierarchical agglomerative clustering (HAC) algorithm is also processed for clustering of users in the public domain by which the overhead decreases effectively. As a final system, this work develops an integrated framework by combining the CP-ABE with AES, multistage encryptor and limited-shuffle. As it is combined with individual methods, this method achieves an efficient performance in the provision of security and data confidentiality.

Recently, Byun presented a privacy maintaining smartcard-based authentication protocol with provable security. We analyze and identify that his scheme is suffering from online password guessing threat, replay threat, and privileged insider threat. It is also not providing user-anonymity and password change phase. To eliminate these above-mentioned security issues, we have designed an extended user anonymous authenticated session key agreement protocol using smartcard. The scalability of our scheme is measured in both formal and informal ways. The formal validation of our scheme has done using Burrows-Abadi-Needham (BAN) logic. Also, simulation is done by automated validation of Internet security protocols and applications (AVISPA) tool. Informal security analysis ensures that our scheme resists to various kinds of fraudulent attacks. The proposed scheme does not only hold up aforementioned security attacks, but also achieves some security features like user-anonymity and easy-to-use password change phase. Our protocol is comparatively more efficient than other schemes in the terms of costs and estimated time.

Business organizations and individual users are using cloud storage for storing their data and files. Cloud storage is managed by cloud service provider (CSP) being third party person to the data owners. Cloud storage consists of user's confidential data. After storing data in cloud, the owner of data cannot have control over data, where owner cannot trust the CSP because possibility of a malicious administrator. Based on this, different schemes are proposed. Security is a major concern for cloud stored data, and CSP has to provide trust to the data owner on security of the cloud stored data. In general, security to data and applications is provided through authentication and authorization. Security through authentication is provided by distributing user name and password to data users. However, the organizational user is not allowed to access all the organizational data. Authorization for accessing the data is provided by using access control models. Regular models are not enough to use the CSP based on the models uses dynamic method and proposed different models using attribute-based encryption (ABE). Earlier access control models cannot be used because of multiple disadvantages. This chapter will discuss dynamic access control model named as RA-HASBE. This model is proved to be scalable and flexible, due to sub-domain hierarchy. It is also proved to be dynamic by permitting user to access the data by risk evaluation using risk engine.

Wireless sensor network (WSN) is a collection of autonomous nodes which are used to sense environmental information for a particular operation or goal. Each node of the sensor network is directly or indirectly connected with base station (BS). The purpose of the BS is to collect required information from the sensor nodes and process its future purpose. Each node of the network contains low capacity of battery. This battery does not fully complete any operation due to its energy capacity, and in the middle of the operation, communication is fails. This event occurs frequently due to sensor nodes energy capacity. It degrades the performance of the network as well as network metrics and raises several types of interference and noise. It causes several types of attacks and hacking. So, to prevent this, in this paper, an intelligent protocol is proposed with the fusion of linear secret sharing (LSS) and elliptic curve techniques. The combination of both techniques helps to overcome the drawback of traditional protocols. Finally, this security protocol helps to reduce the overhead of WSN and enhances several security mechanisms against different conflicting attacks.

Wireless sensor network (WSN) is a part of wireless network which has flexible and dynamic nature in context of real-life applications. It has several usages in terms of user requirements. It consists of several nodes having limited energy capacity. Energy capacity of the nodes does not completely fulfil the requirement of the services. During transaction or transmission, data is dropped and fails to reach the destination node or base station (BS). This BS also suffers several types of difficulties for sending or receiving data packets. So, there is need of some techniques or modelling that help to protect this issue. Apart from energy, distance is also one important parameter for transmitting data successfully. Although energy is the crucial parameter, but, combination of both energy and distance plays an important role for managing efficient route of the network. The proposed method is the combination of intelligent technique as well as mathematical modelling that uses fuzzy logic as an intelligent technique and quadratic programming as a mathematical modelling for solving the proposed goal. The combination of both provides a robustness technique that uses two basic parameters, energy and distance, for selecting optimal route of the WSN. The proposed method is validated in LINGO optimization software for formulating and validating the model efficiently.

Routing is a predominant challenge in the field of WSNs because of insufficient power supply in each node. And low-transmission bandwidth required less memory space and handling limit. These sensors distributed randomly in nature and the environment, and each sensor nodes gather data from that environment for further analysis and additional processing and transmits the information and data to the base station. We discussed the different machine learning algorithms to develop routing protocols for the WSNs. These technologies have allowed the sensor to learn the experience data to make appropriate routing decisions and respond to changing the environment. We covered a wide range of machine learning (ML)-based routing protocols, such as distributed regression (DR), self-organizing map (SOM), and reinforcement learning (RL). This chapter affords a complete evaluation of the literature on the topic. The review has structured in such a way that suggests how network characteristics and necessities gradually viewed over time.

Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for the various application. Faults occurring to sensor nodes are prevalent due to the sensor device itself and the harsh environment, where the sensor nodes are deployed. To ensure the quality of service and to avoid further degradation of service, it is necessary for the WSN to be able to tolerant of the faulty nodes present in the network. The fault diagnosis techniques are classified based on the methods they employ to determine the faults. In this paper, we have proposed a traversal-based diagnosis algorithm that seeks to diagnose both permanent as well as intermittent fault in WSN. The proposed algorithm employs a special node called an anchor node to traverse the field. The traversal of the field is decided by a proposed traversal algorithm taking into consideration the length and breadth of the sensor field, and the transmission range of the nodes. The anchor node stops at defined positions in the deployment field where it executes the fault diagnosis algorithm taking into consideration the normal sensor nodes which are in its range. The diagnosis algorithm uses a timeout mechanism to identify hard faults and adjusted boxplot method to identify permanent and intermittent faults in the network. The adjusted boxplot method takes into consideration the skewness of the data generated by the nodes in the sensor field. The faulty sensor nodes are classified by using a Feed Forward Neural Net (FFNN) model with Gravitational Search (GS) learning algorithm. The proposed algorithm is implemented in the Omnet++ environment which shows very promising results. The performance parameters, such as detection accuracy, false alarm rate, false positive rate, and energy consumption of the proposed algorithm show significant improvement over the existing algorithms.

The goal of cloud controller is to focus on continuous delivery of services to user on demand basis followed by “pay-per-use” model. Due to the increasing demand of cloud services, energy consumption on data center is increasing rapidly which lead to high operational cost. The harmful emission from this energy intensive data center affects our environment badly and cause climate change significantly. So as an alternative we have focused on onsite green power generation to reduce the harmful effects of greenhouse gases. In this paper, we proposed a fuzzy Q-learning based self-learning controller to optimize the load for specific data center. The proposed method also helps to reduce uncertainty and solve the congestion issue efficiently through fuzzy linguistic behavior and membership function. In this proposal, fuzzy output parameter considered as reward value which is used to learn and update the state for each data centre.

Wireless sensor networks monitor environments that amendment apace over time. This dynamic behavior of the networks is either caused by external factors or initiated by the system itself. Machine learning techniques help us to work with extreme conditions and assist in avoiding the redesign of the network. The prominent feature of training the machine or network itself to modify according to such kinds of environments is being introduced in the sensor networks using machine learning techniques. However, the performance of the sensor networks has many constraints like energy efficiency, information measure or bandwidth, etc. Localization of nodes is one of the major issues that have to be worked on, as proper placement of nodes solves above-mentioned performance issues. The sensors in wireless networks gather knowledge regarding the objects they are to be sensed by which machine learning algorithms conjointly evoke several sensible solutions for localization of nodes that maximize resource utilization and prolong the lifetime of the network. The machine learning algorithms are categorized into three categories, namely supervised learning, unsupervised learning and reinforcement learning algorithms. As localization is the method of deciding the geographic coordinates of network’s nodes and its relevant components as position awareness of sensing element of every sensor nodes plays a vital role in network communication for further process. In this chapter, we are going to focus on how the localization issue in wireless sensor networks can be solved using the three categorized machine learning algorithms.

In this intelligent era, vehicles are exploited for a different mobile sensor activity. Vehicular delay tolerant network is the application of delay tolerant network. Nowadays, when conventional network does not work or fails in the emergency situation, vehicular delay tolerant networks provide solutions. Vehicular delay tolerant networks is very useful for solving many problems such as sensor-based applications, intelligent traffic, weather forecasting and many more delay tolerant services like campus information services etc. Many more delay adaptive services to save infrastructure-based network load, and these type of networks are very successful. For efficient routing strategy, the efficient selection of vehicular relay node is very important. So in this chapter, we have proposed “vehicular delay tolerant network-based communication using machine learning classifiers.” First, we have analyzed which machine learning classifier is the best solution for our problem. We have used machine learning classifiers for filtering efficient vehicular nodes, so that packets can be delivered from source to destination.

In recent years, Internet of things (IoT) technology is the fastest growing technology which connects physical device or sensors to Internet. IoT devices collect the information from object’s then store or transfer information over the Internet without help of any manual involvement and with the help of embedded technology. The big data play a vital role in IoT because it is a process of a huge amount of information on real-time basis. This chapter highlights the use of big data and IoT for the power systems. IoT can be used in various areas of power system such as metering, transformer monitoring, prediction of demand and planning for future consumption. The main objective of this chapter to make a clear understanding of the use of big data and IoT in the power system and how it will improve customer service and social welfare.

Wireless sensor network (WSN) is a collection of sensor nodes that are attached with base station (BS) and sink node to achieve a specific purpose. The main purpose of the WSN is sensing environmental parameters such as energy, temperature, and humidity. There are several parameters of the WSN that changes time to time and frequently based on the operation. Each sensor node contains limited capacity of battery that is insufficient during any operation and fails to send the data packet to the BS. So, there is need of some modeling using some intelligent technique. In this paper, a fuzzy quadratic programming (FQP) is used to optimize network parameters efficiently. FQP is the fusion of fuzzy logic and quadratic programming. Fuzzy logic is a multi-values logic which is used to reduce uncertainty and estimate imprecise parameters efficiently. Quadratic programming is a nonlinear programming based on second order of mathematical polynomial for reducing the main objective. The combination of both helps to analyze conflicting network parameters and decide the optimal objective value along with constraints. The proposed method is validated in LINGO optimization software in terms of several rounds to predict the optimal solution.

Industry control systems (ICS) are considered as one of the inevitable systems in this contemporary smart world. In that supervisory control and data acquisition (SCADA) is the centralized system that control the entire grid. When a system is considered to be a whole and sole control, obviously an uncompromised security would be the prime. By having that as a major concern, a lot of research is being done on IDS security. In spite of that it has several cons including increased fake positive and fake negative rates, which will invariably lead to a larger chaos. To get rid of these problems, a weighted-intrusion based cuckoo search (WI-CS) and graded neural network (GNN) methods are proposed in this chapter. The key purpose of this chapter is to identify and categorize the anomalies in a SCADA system through data optimization. At initial stage, the collected real-time SCADA dataset is given as input. Then, by using the aforementioned proposed machine learning algorithms, these data are clustered and optimized. Later to find, the type of intrusion will remain as a further challenge and for that we propose HNA-AA algorithm. The investigational results estimate the efficiency of the system by considering sensitivity, false detection rate, precision, recall, Jaccard, accuracy, dice and specificity.

Mobile ad hoc network (MANET) has been a challenging field with its foremost criteria like heterogeneity of nodes, dynamic topology, energy constraint and security over the years. MANETs are globally popular for their cost-effectiveness ease of access and configuration. However, MANETs are vulnerable to many types of attacks like Blackhole, Wormhole, Grayhole, etc., which makes MANETs pretty much risky to rely upon when scaling up on a large scale. Under mobile ad hoc networks, all the transmission between the mobile nodes occurs wirelessly. Due to the infrastructure-less, self-organizing and dynamic nature of the nodes, it is an arduous task to enforce any security solutions against these kinds of vulnerabilities. Ad hoc on-demand vector (AODV), a supremely significant route-on-demand routing protocol for MANET, relies on the routing table at each intermediate node location. In this paper, we are mainly analyzing the performance of a MANET under Grayhole attack as per AODV routing protocol using NS-2 simulation environment.

When the fourth generation for wireless communication networks was developed, it was upgraded to provide both enhanced coverage area and higher data rates to every mobile user with lower latency. However, wireless communication system for the next-generation network will need to challenge new requirement with a greater diversity of application requirements such as ultra-high data rate, ultra-low latency, flexible use of spectrum and spectrum sharing, and battery-powered sensors that needs extremely low energy consumption, and some other control applications that want a very short round trip time (RTT). Due to problems with orthogonal frequency division multiplexing (OFDM) and next-generation demands, OFDM is not used as a promising waveform for next-generation wireless communication network. In these circumstances, alternative multiplexing schemes such as generalized frequency division multiplexing (GFDM), due to flexibility in pulse shape and single cyclic prefix in a multi-path system, GFDM is becoming common every day, making it eligible for 5G wireless technologies. GFDM looks as generalization of OFDM technique. But one of the common drawbacks of every multicarrier system is their high peak to average power ratio (PAPR). The main effect of strong PAPR is instability in the analog to digital converter (ADC) and digital to analog converter (DAC), decreased its performance and raised costs. A PAPR reduction technique such as clipping and filtering that greatly improves the efficiency compared to the initial GFDM signal PAPR. Overall peak re-growth can be reduced by using repeated clip and filter operations. Simulation is performed for this scheme to evaluate this system’s PAPR output for different values of roll-off variables.

The present chapter deals with the time series of worldwide monthly mobile data traffic per smartphone during January, 2014-December, 2019. Firstly, an attempt is made to understand the nature of memory in this time series by taking into account scaling pattern and the issue of persistence or anti-persistence by means of Hurst exponent. Next its self-similarity or self-affinity which is coined as fractal behaviour is analysed using Higuchi’s method of fractal dimension estimation. Next the self-organized criticality (‘soc’) of the present data is analysed with the help of the integrated (cumulative) distribution. To examine the nonlinearity and deterministic/stochastic nature of the governing mechanism, we use delay vector variance (DVV) method. We have taken into account 0–1 test for chaos and recurrence plot (RP) analysis with recurrence quantification analysis (RQA) to test the signature of chaos in the present data. In fine, the proposed chapter employs certain statistical signal processing techniques to realize the memory, self-similarity, self-organized criticality, nonlinearity and chaos in the present time series of worldwide monthly mobile data traffic per smartphone. This study possibly indicates a persistent, self-similar, deterministic, nonlinear and non-chaotic profile with no ‘soc’ for the present time series.