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Scheduling Based On Particle Swarm Optimization And Energy Management For Wireless Sensor Network Node

Posted on:2009-01-30Degree:MasterType:Thesis
Country:ChinaCandidate:X M JiangFull Text:PDF
GTID:2208360245460975Subject:Detection Technology and Automation
Abstract/Summary:PDF Full Text Request
Wireless sensor networks (WSN) is an integration of sensor technologies, nested computation technologies, distributed computation technologies and wireless communication technologies. It is a new technology for information acquirement and processing. Because of its high importance in many applications, WSN becomes a new research hotspot, and is recognized as one of the most influential technologies in 21st century.WSN depends on its batteries for energy supply, but the energy of batteries is limited. And once the sensor nodes are deployed, it is difficult to change or recharge the batteries because of the restrictions of its working environment. Thus the core issue in WSN software and hardware designing is to reduce its energy cost. Because of the high density of sensor nodes in WSN, there are quantities of redundant sensor nodes. All sensor nodes working simultaneously will lead to great waste of energy. One of the efficient methods for energy management is to schedule some nodes into sleep mode while others keep active and make the node work in turns. The purpose of node scheduling is to schedule as many as possible redundant nodes into sleep mode, thus to reduce unnecessary energy cost, and to extend the lifetime of the networks.This article is based on classic LEACH protocol. Under the circumstances of coverage and the principle of minimizing energy cost in a cluster, node scheduling could be turned into optimization problem. Particle Swarm Optimization (PSO) is chosen to solve this problem, because of its excellence in complicated optimization. An improved PSO algorithm is also proposed in this article. When running this new algorithm, different inertia weight values are given to particles according to their fitness. Thus the algorithm is engaged with both good exploration ability and good exploitation ability. When the optimum information of the swarm is stagnant, mutation operator is introduced to change the location and speed of the particles which are close to the local optimum position, and thus to reduce the possibility of trapping at the local optimum. Simulation proves that the new algorithm is efficient in saving energy. Compared to standard PSO, its global searching ability and the speed of convergence is significantly improved, and the premature convergence problem is effectively avoided. In addition, according to the rationalization of cluster-head selection, this article introduces a cluster-head rotation mechanism, which is based on energy balance. Through simulation and comparison with other algorithms, it is obvious that this algorithm can effectively balance energy consumption and extend the lifetime of WSN.
Keywords/Search Tags:wireless sensor networks, energy management, node scheduling, particle swarm optimization, cluster-head rotation
PDF Full Text Request
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