Please use this identifier to cite or link to this item:
Title: Improved Nature Inspired Algorithms For Optimization Problems In Wireless Sensor Networks
Authors: Kanchan, Pradeep
Supervisors: D, Pushparaj Shetty
Keywords: Optimization;Quantum computing;Nature inspired algorithms;WSN
Issue Date: 2022
Publisher: National Institute of Technology Karnataka, Surathkal
Abstract: In a Wireless Sensor Network (WSN), the nodes are placed in random positions and con- nected to each other through networks. The nodes collect data from each other, perform pro- cessing and the results are sent to a Base Station (BS). In simple words, Optimization is selecting the best element, with respect to some criterion, from a given set of alternatives. Most of the research in the field of WSNs have concentrated on optimizing clustering, energy efficiency, network lifetime, coverage, load balancing, fault tolerance, quality of service, etc. Multi Objective Optimization deals with optimizing more than one objective at the same time. This thesis concentrates on developing nature inspired algorithms for energy efficient clus- tering and for improving network lifetime in conjunction with Quantum computing. Also, the aim is to develop an efficient nature inspired algorithm for optimizing target coverage in Ho- mogeneous as well as Heterogeneous WSN using Quantum Computing. For achieving the first 2 objectives (Optimizing Energy Efficiency and Improving Network Lifetime), the nature inspired algorithm, PSO (Particle Swarm Optimization) is used in con- junction with Quantum computing. For the 3rd objective (Optimizing Target Coverage), an- other nature inspired algorithm, MOEAD (Multi Objective Evolutionary Algorithm with De- composition) is used in conjunction with quantum computing.
Appears in Collections:1. Ph.D Theses

Files in This Item:
File Description SizeFormat 
155055MA15F05-Pradeep Kanchan.pdf3.21 MBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.