Improved Nature Inspired Algorithms For Optimization Problems In Wireless Sensor Networks

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.