Research On WSN Clustering Algorithm Inspired By Swarm Intelligence

Wireless Sensor Networks（WSN）is a network formed by self-organization of a large number of sensor nodes,which can realize real-time monitoring and data collection of specific areas,and provide strong data support for the realization of interconnection of everything.Due to the small size of the sensor nodes in the network and the inability to replace the battery after the deployment of the sensor nodes,it is difficult to replenish the energy.Therefore,reducing energy consumption and extending network lifetime are the key issues of WSN.Clustering nodes in WSN has been proved to be able to improve the above problems.Researchers have proposed some clustering and routing protocols for WSN,but most protocols have room for improvement and optimization.Particle swarm optimization（PSO）algorithm has the advantages of easy implementation and fast convergence,which is suitable for combinatorial optimization,network clustering and routing.Therefore,this thesis proposes a novel WSN clustering algorithm（IPSO-LF）by introducing the principle of PSO,in order to reduce energy consumption and extend the network lifetime.The main work is as follows:1.Inertia weightω,acceleration coefficients c₁ and c₂ are adjusted from fixed values to adaptive variables,and then the Lévy Flight operator is introduced to improve PSO to IPSO,which improves the problem that PSO is easy to converge to local optimum.2.A novel WSN clustering algorithm（IPSO-LF）is proposed.The innovation of IPSO-LF is as follows:（1）IPSO is applied to the WSN clustering,（2）a novel fitness function is designed,（3）the analytic hierarchy process（AHP）is used to calculate the weight of fitness.3.IPSO-LF is simulated and compared with PSO,GWO,PSO-ECHS and WGWO.The simulation results show that IPSO-LF has better performance in energy consumption,end to end delay（ETE）,packet delivery ratio（PDR）and network lifetime.