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Master's Thesis from the year 2013 in the subject Computer Science - Miscellaneous, grade: -, , course: Master of Technology, language: English, abstract: Wireless Sensor Networks (WSNs) are highly integrated technologies applying sensors, microcontrollers and wireless networks technologies. Wireless sensor networks (WSNs) is a promising technology that has a large spectrum of applications such as, battlefield reconnaissance, border protection and security surveillance, preparing forecasts, severe environment detection, volcano monitoring, disaster management. WSNs operate unattended in harsh environments with limited energy supplies that can’t be practically changed or recharged. Thus energy efficiency is a critical design issue which must be addressed. Clustering plays an effective role in judicious use of dwindling energy resources of the deployed sensor nodes. Nodes are grouped into clusters and a specific designated node, called the cluster head is responsible for collecting data from the nodes in its cluster, aggregating them and sending to the BS, where data can be retrieved later. Besides energy efficiency, clustering has many other advantages like reduced routing overhead, conservation of communication bandwidth, stabilized network topology, network stability etc In this research, we study the energy efficiency of two clustering algorithms, S-Web and LEACH and compare them for network lifetime. Simulation results show that the S-Web clustering mechanism achieves a noticeable improvement in the network lifetime.
Wireless Sensor Networks have a wide range of applications in different areas. Their main constraint is the limited and irreplaceable power source of the sensor nodes. In many applications, energy conservation of the sensor nodes and their replacement or replenishment due to the hostile nature of the environment is the most challenging issue. Energy efficient clustering and routing are the two main important topics studied extensively for this purpose. This book focuses on the energy efficient clustering and routing with a great emphasis on the evolutionary approaches. It provides a comprehensive and systematic introduction of the fundamentals of WSNs, major issues and effective solutions.
Wireless networking covers a variety of topics involving many challenges. The main concern of clustering approaches for mobile wireless sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach, the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. In Mobile, Wireless and Sensor Networks: A Clustering Algorithm for Energy Efficiency and Safety, the authors use an approach based on computing of the weight of each node in the network as the proposed technique to deal with this problem. They present a virtual laboratory platform (VLP) of baptized mercury, allowing students and researchers to make practical work (PW) on different aspects of mobile wireless sensor networks. The authors’ choice of WSNs is motivated mainly by the use of real experiments needed in most college courses on WSNs. These usual experiments, however, require an expensive investment and many nodes in the classroom. The platform presented here aims at showing the feasibility, the flexibility, and the reduced cost using the authors’ approach. The authors demonstrate the performance of the proposed algorithms that contribute to the familiarization of the learners in the field of WSNs. The book will be a valuable resource for students in networking studies as well as for faculty and researchers in this area.
Wireless sensor network (WSN) is a low-powered network formed by the sensor nodes that finds application in civilian, military, visual sense models, and many others. Improved network lifetime is an important task to be achieved by these sensor networks. In this paper, a methodology for evaluation of clustering efficiency, routing efficiency, energy efficiency, and the lifetime of two dense wireless sensor network fields?using a distributed clustering approach, the hybrid energy efficient clustering algorithm (HEECA)?was proposed, which mainly targeted effectively prolonging the lifetime of wireless sensor networks. It is a well-distributed and energy-efficient clustering algorithm that employs three novel techniques: zone based transmission power (ZBTP), routing using distributed relay nodes (DRNs), and rapid cluster formation (RCF). The proposed scheme was compared with two well-evaluated existing distributed clustering algorithms, O-LEACH and HEED. Simulation results clearly showed an excellent improvement in remaining energy, throughput, routing efficiency, and energy efficiency of the entire wireless sensor system. The clustering process was effectively controlled, thereby the number of cluster heads selection and the number of packets delivered to the base station was also found to be effective. Ultimately, the overall lifetime of the wireless sensor network was improved when compared to the two existing distributed clustering algorithms.
In this thesis, we propose a suite of Evolutionary Algorithms (EA)-based protocols to solve the problems of clustering and routing in Wireless Sensor Networks (WSNs). At the beginning, the problem of the Cluster Heads (CHs) selection in WSNs is formulated as a single-objective optimization problem. A centralized weighted-sum multi-objective optimization protocol is proposed to find the optimal set of CHs. The proposed protocol finds a predetermined number of CHs in such way that they form one-hop clusters. The goal of the proposed protocol is to enhance the network's energy efficiency, data delivery reliability and the protocol's scalability. The formulated problem has been solved using three evolutionary approaches: Genetic Algorithms (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) and we assessed each of their performance. Then, a PSO-based hierarchical clustering protocol that forms two-hop clusters is proposed to investigate the effect of the number of CHs on network's energy efficiency. This protocol enhances the WSN's energy efficiency by setting an upper bound on the number of CHs and trying to minimize the number of CHs compared to that upper bound. It also maximizes the protocol's scalability by using two-hop communication between the sensor nodes and their respective CHs. Then, a centralized weighted-sum PSO-based protocol is proposed for finding the optimal inter-cluster routing tree that connects the CHs to the Base Station (BS). This protocol is appropriate when the CHs are predetermined in advance. The proposed protocol uses a particle encoding scheme and defines an objective function to find the optimal routing tree. The objective function is used to build the trade-off between the energy-efficiency and data delivery reliability of the constructed tree. Finally, a centralized multi-objective Pareto-optimization approach is adapted to find the optimal network configuration that includes both the optimal set of CHs and the optimal routing tree. A new individual encoding scheme that represents a joint solution for both the clustering and routing problems in WSNs is proposed. The proposed protocol uses a variable number of CHs, and its objective is to assign each network node to its respective CH and each CH to its respective next hop. The joint problem of clustering and routing in WSNs is formulated as a multi-objective minimization problem with a variable number of CHs, aiming at determining an energy efficient, reliable (in terms of data delivery) and scalable clustering and routing scheme. The formulated problem has been solved using two state-of-the-art Multi-Objective Evolutionary Algorithms (MOEA), and their performance has been compared. The proposed protocols were developed under realistic network settings. No assumptions were made about the nodes' location awareness or transmission range capabilities. The proposed protocols were tested using a realistic energy consumption model that is based on the characteristics of the Chipcon CC2420 radio transceiver data sheet. Extensive simulations on 50 homogeneous and heterogeneous WSN models were evaluated and compared against well-known cluster-based sensor network protocols.
Resource optimization has always been a thrust area of research, and as the Internet of Things (IoT) is the most talked about topic of the current era of technology, it has become the need of the hour. Therefore, the idea behind this book was to simplify the journey of those who aspire to understand resource optimization in the IoT. To this end, included in this book are various real-time/offline applications and algorithms/case studies in the fields of engineering, computer science, information security, and cloud computing, along with the modern tools and various technologies used in systems, leaving the reader with a high level of understanding of various techniques and algorithms used in resource optimization.
This book is a collection of accepted papers that were presented at the International Conference on Communication and Computing Systems (ICCCS-2016), Dronacharya College of Engineering, Gurgaon, September 9–11, 2016. The purpose of the conference was to provide a platform for interaction between scientists from industry, academia and other areas of society to discuss the current advancements in the field of communication and computing systems. The papers submitted to the proceedings were peer-reviewed by 2-3 expert referees. This volume contains 5 main subject areas: 1. Signal and Image Processing, 2. Communication & Computer Networks, 3. Soft Computing, Intelligent System, Machine Vision and Artificial Neural Network, 4. VLSI & Embedded System, 5. Software Engineering and Emerging Technologies.
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.
The book includes papers on a wide range of emerging research topics spanning theory, systems and applications of computing and communication technologies viz. Nonlinear Dynamics in Cryptography, Discrete domain Swarm Robotics, Machine Learning, Facility Layout Problem, Crowdfunding Projects, Deep Learning, MHD Nanofluid Flow, Medical Diagnostics, Human Computer Interface, Social Networking, System Performance, Wireless Sensor Networks, Cognitive Radio Networks, Antenna Design etc.; presented at the 11th International Conference on Advanced Computing and Communications Technologies (11th ICACCT 2018) held on 17-18 February, 2018 at Asia Pacific Institute of Information Technology, Panipat, India.
This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.