CO-EVOLUTIONARY PARTICLE SWARM OPTIMIZATION WITH FUZZY MULTIPLE PARAMETER DECISION-MAKING TOAVOID LOAD AND BANDWIDTH CONSUMPTION IN WSN
The Wireless Sensor Network (WSNs) basically includes wireless communication capabilities, computation process and nodes with sensing capabilities. Data dissemination protocols, power management, and many routing process have been particularly designed for WSNs where load and bandwidth consumption is an important design issue. Thus, in this paper introduce a distributed energy-efficient clustering algorithm such as Fuzzy Multiple Parameter Decision-Making (FMPDM) for selecting an optimal cluster algorithm. For cluster head selection process considering different kinds of parameters such as Initial Energy, Average Energy of the Network,Energy Consumption Rate and Residual Energy. After this cluster head selection process other cluster nodes are selected by using Co-Evolutionary Particle Swarm Optimization (CEPSO) algorithm to avoid the load and bandwidth consumption. The simulation results shows that this proposed method is more effective in term of avoiding bandwidth and load consumption. In this process use NS2 simulation with differentkinds of metrics such aspacket delivery ratio, network lifetime and energy consumption.
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