Sudhanshu Tyagi

Review: A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks

In recent years, Wireless Sensor Networks (WSNs) have emerged as a new powerful technology used in many applications such as military operations, surveillance system, Intelligent Transport Systems (ITS) etc. These networks consist of many Sensor Nodes (SNs), which are not only used for monitoring but also capturing the required data from the environment. Most of the research proposals on WSNs have been developed keeping in view of minimization of energy during the process of extracting the essential data from the environment where SNs are deployed. The primary reason for this is the fact that the SNs are operated on battery which discharges quickly after each operation. It has been found in literature that clustering is the most common technique used for energy aware routing in WSNs. The most popular protocol for clustering in WSNs is Low Energy Adaptive Clustering Hierarchy (LEACH) which is based on adaptive clustering technique. This paper provides the taxonomy of various clustering and routing techniques in WSNs based upon metrics such as power management, energy management, network lifetime, optimal cluster head selection, multihop data transmission etc. A comprehensive discussion is provided in the text highlighting the relative advantages and disadvantages of many of the prominent proposals in this category which helps the designers to select a particular proposal based upon its merits over the others.

EHE-LEACH: Enhanced heterogeneous LEACH protocol for lifetime enhancement of wireless SNs

Wireless Sensor Network (WSNs) are collection of small Sensor Nodes (SNs) which are capable of performing multiple tasks such as data aggregation, processing and communication either to other SNs or to the Base Station (BS). As SNs are battery operated, so efficient utilization of energy during various operations in WSNs is one of the major issues which requires special attention. It has been observed in literature that Hierarchical clustering and the node heterogeneity are the two parameters by which the lifetime of WSNs can be enhanced. Keeping in view of the above, in this paper, we propose an Enhanced Heterogeneous LEACH (EHE-LEACH) Protocol for Lifetime Enhancement of SNs. A fixed distance based threshold is used for the bifurcation of direct communication and cluster based communication in the proposed scheme. SNs near to the BS communicate directly and those which are far away from the BS use cluster based communication. To evaluate the performance of the proposed scheme two key parameters known as: Half Nodes Alive (HNA) and Last Node Alive (LNA) are selected. By selecting the distance based threshold with the ratio of 1:9 between direct communication and cluster based communication it has been observed that EHE-LEACH has better network lifetime with respect to various parameters in comparison to the other well known proposals such as LEACH and SEP.

LA-EEHSC

Wireless sensor networks (WSNs) consist of many sensor nodes (SNs) which may be deployed at different geographical locations to perform multiple tasks such as monitoring, data aggregation, and data processing. During all these operations, energy of the SNs continuously depleted which results in the creation of energy holes in some regions. As SNs are battery operated and it is difficult to replace the battery of the SNs each time, so energy conservation is a paramount concern to increase the lifetime of the WSNs. It has been proved in the literature that clustering of SNs can be used for energy saving during various operations in WSNs. Keeping in view of the above issues, in this paper, we propose a new learning automata-based energy efficient heterogeneous selective clustering (LA-EEHSC) scheme for WSNs. Automaton is assumed to be located on each SN with two types of SNs, namely, normal and advanced are considered in the proposed scheme. Based upon the weighted election probability (WEP) of each group of SNs, Cluster Heads (CHs) are selected among the group of SNs by the automaton. Automaton at each SN receives reward or penalty from the environment based upon WEP of different SNs. An efficient learning automata-based energy efficient clustering algorithm is also proposed. Finally, first node die (FND) and last node alive (LNA) are selected as the key parameters for the measurement of lifetime of network field. Using these parameters, we have evaluated the performance of the proposed scheme in different network scenarios in comparison with the well-known existing protocols such as LEACH, LEACH-SC and SEP. The results obtained show that proposed scheme yields 5.89% improvement in lifetime and 21.14% improvement in stability in comparison to LEACH, LEACH-SC, and SEP.

Bayesian Coalition Game-based optimized clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have gained a lot of popularity in recent years because these are being used in wide range of applications. A collection of randomly/planned deployed tiny Sensor Nodes (SNs) can perform the task according to the need of a specific application. Utilization of energy of SNs is one of the key issues in these networks as nodes are battery operated and recharge or replacement of battery is a difficult task to be achieved. To address this issue, we propose a Bayesian Coalition Game-based optimized clustering in WSNs. To formulate the game, we propose a new Hybrid Homogeneous LEACH (HHO-LEACH) protocol for SNs in WSNs. We have used the concepts of Learning Automata (LA), and Bayesian Coalition Game (BCG) in which SNs are assumed as the players in the game with dynamic thresholds-based coalition formation among themselves, i.e., coalition among the nodes are formed using distance-based thresholds which makes a partition of the network field. SNs near to base station use direct communication for data transfer with or without single hop to the Base Station(BS) after interacting with the environment. During this process, each player may get a reward, or a penalty with respect to the finite number of actions performed. Performance of the proposed protocol is evaluated using extensive simulations by selecting various evaluation metrics. The results obtained show that proposed coalition game achieved better stability, and network lifetime in comparison to other existing protocols such as LEACH, and DD.

The Role of Internet of Things and Smart Grid for the Development of a Smart City

The smart grid (SG) and Internet of things (IoT) are arguably most important feature in any smart city (SC). If energy unavailable for a significant period of time, all other associated functions will be eventually affected. On the other side, IoT ensures integration of diversified networks, wireless technologies, and appliances. This enables smart communication between various devices, so that they can smartly communicate with each other. Smart city is one of the applications of IoT. Smart city encompasses improved infrastructure for better economic growth and quality life style. Recent technological trends have led to the rise in demand for efficient communication between devices without any human intervention, thus creating a huge market for IoT. With the abundance of opportunities for growth, every developed country, even some of the developing countries, are now come up with their own fully funded IoT projects. This paper summarized the role of IoT for the development of smart city and also presents architecture for the smart city for efficient utilization of infrastructure and challenges of IoT infrastructure development with their solution; further it enlists applications of IoT based on their domain.

An advanced Internet of Thing based Security Alert System for Smart Home

Before inception of Internet of Things (IoT), personal computers and laptop were used to handle daily tasks of individuals like mail surfing, access to bank portal, observing current temperature, among others. Nowadays, IoT-enabled smart devices like smart mobile phones, PDAs, and tablets are being used by them for such tasks due to rapid growth in IoT. Smart homes have been widely accepted by individuals and organizations world wide due to their many advantages. Home security systems can be defined as monitoring of complete home/some portion of home from a remotely located or centralized location. It allows the user to watch all activities inside the home from a remote location that ultimately gives satisfaction to the owner of the home. Many home security systems exist, but they have some challenging issues like: delay, non-web enabled and difficult to handle during transfer of alerts to user in situation where any unusual event occurred inside the home. If any unusual event encountered inside the home, where security systems deployed, then system must be capable enough to send alert to the user without any delay by phone, text, or email. Cameras and other latest network technologies have enabled us to remotely monitor the home more effectively and efficiently from our smart phone. Hence, considering the above mentioned facts, in this paper, we have proposed an advanced Internet of Thing based Security Alert System for Smart Home in order to detect an intruder or any unusual event at home, when nobody is available there. This low-cost home security system utilizes a small pyroelectric Infrared (PIR) module and raspberry pi for minimizing the delay during process of e-mail alert. This paper also confirms the advantage of Raspberry Pi flexibility and broad probability of its usage. Preliminary analyses have shown encouraging results.

Online Signature-Based Biometric Recognition

This chapter covers the online signature-based biometric recognition system, which includes fingerprint scanning, facial geometry, footprint scanning, retina and iris patterns, DNA, and heartbeat scans. It covers the background knowledge of signature verification system along with its classification. A systematic comparison between online and offline signature verification system is also included. Basics of biometric recognition in terms of security, exploring definitions of the various parameters that can be used in the security perspective, are also covered (Obaidat and Boudriga, Security of e-Systems and Computer Networks. Cambridge University Press, 2007; Obaidat and Sadoun, IEEE Trans Syst Man Cybernetics Part B, 27(2):261–269, 1997). An in-depth discussion about the online signature-based authentication methods is also presented. Finally, some case studies have been discussed to demonstrate the concept.

Ethical, Legal, and Social Implications of Biometric Technologies

This chapter covers the ethical, legal, and social implications of biometric technologies, which include fingerprint scanning, facial geometry, footprint scanning, retina and iris patterns, DNA, and heart beat scanning, among others. The introduction part of the chapter will address the process of how to do the enrollment in biometric technology as well as the matching process. At the end of introduction, different biometric techniques and stacks have been given. The next section describes the legal implications of biometric technology to the society. Social issues and uses of biometric technology in government agencies are included in two distinct sections. Different standards formed by the European government were also summarized. The general perception about the biometric technology has been covered in this chapter. Lastly business ethics, biometric security, and a couple of case studies like megaproject UIDAI of India and the utilization of biometric-based security system for banking systems have been covered. By the end of this chapter, readers will get the information about ethical, legal, and social implications on the biometric technologies along with the detailed knowledge of megaproject UIDAI of India and the utilization of biometric-based security system for banking systems.

Fog computing for Healthcare 4.0 environment: Opportunities and challenges

Abstract Internet of things provides interaction with billions of objects across the world using the Internet. In Internet of thing era, Healthcare Industry has grown-up from 1.0 to 4.0 generation. Healthcare 3.0 was hospital centric, where patients of long-lasting sickness suffered a lot due to multiple hospital visits for their routine checkups. This in turn, prolonged the treatment of such patients along with an increase in the overall expenditure on treatment of patients. However, with recent technological advancements such as fog and cloud computing, these problems are mitigated with a minimum capital investment on computing and storage facilities related to the data of the patients. Motivated from these facts, this paper provide an analysis of the role of fog computing, cloud computing, and Internet of things to provide uninterrupted context-aware services to the end users as and when required. We propose a three layer patient-driven Healthcare architecture for real-time data collection, processing and transmission. It gives insights to the end users for the applicability of fog devices and gateways in Healthcare 4.0 environment for current and future applications.

Dimensionality Reduction Using PCA and SVD in Big Data: A Comparative Case Study

With the advancement in technology, data produced from different sources such as Internet, health care, financial companies, social media, etc. are increases continuously at a rapid rate. Potential growth of this data in terms of volume, variety and velocity coined a new emerging area of research, Big Data (BD). Continuous storage, processing, monitoring (if required), real time analysis are few current challenges of BD. However, these challenges becomes more critical when data can be uncertain, inconsistent and redundant. Hence, to reduce the overall processing time dimensionality reduction (DR) is one of the efficient techniques. Therefore, keeping in view of the above, in this paper, we have used principle component analysis (PCA) and singular value decomposition (SVD) techniques to perform DR over BD. We have compared the performance of both techniques in terms of accuracy and mean square error (MSR). Comparative results shows that for numerical reasons SVD is preferred PCA. Whereas, using PCA to train the data in dimension reduction for an image gives good classification output.