Research On Energy Efficiency Related Key Issues And Algorithms In Wireless Sensor Networks

The wireless sensor networks is composed by the multiple energy limited nodes,and it has been widely used in the medical care,military,industrial and other fields.The energy limitation of sensor nodes and the large energy consumption in the process of communication are the core issue of wireless sensor network performance.Therefore,how to reduce energy consumption of nodes and prolong the network life are the focus of the present study.This dissertation focuses on the optimization of network topology,the node energy consumption and the network node positioning and data transmission.According to this investigation,the research in this dissertation put forward the following four research innovations: the topology control research based on the scale-free theory,using the theory of compressive sensing to reduce the system energy of communication consumption,the node deployment research based on the firefly algorithm and the data transmission method based on hybrid leapfrog algorithm.The main innovation points and research content is as follows:(1)The energy efficient wireless sensor networks scale-free topology based on the complex network theory is put forward.The network energy consumption is effectively balance with full consideration of the factors such as the node residual energy and the node degree in the process of topological evolution.At the same time,the scale-free network is studied in the application of energy heterogeneous WSN.Through building the node lifetime model,we conclude that the node lifetime is affected by the node residual energy and the communication distance.Additionally,the both ends of the node lifetime are introduced as the edge weights influencing factors.And then through the edge weight controlling the node weight,the weighted scale-free topology with energy consumption balanced characteristics is obtained.(2)Taking the practical application about the field of vibration signal detection in wireless sensor networks as the example,the compressed sensing theory is applied to data compression process in order to reduce the energy consumption of communication,prolong the lifetime of the network nodes.Considering that reconstruction accuracy of existing data reconstruction in compressed sensing can be easily influenced by sparsity,after analysis of compressed sensing data reconstruction principle,with sub-frame processing the original signal in fixed length to reduce the solution space,and applying quantum theory encoding in Particle Swarm Optimization.(3)The performance of improved glowworm swarm optimization(GSO)algorithm and its application in coverage optimization of WSNs and the global convergence analysis of basic GSO are analyzed in this paper.And a new coverage optimization algorithm for WSNs is presented according to the analysis of GSO.A model of coverage optimization in WSNs is built up by taking node uniformity and network coverage rate as the criterion,and the relationship between node redundancy and network coverage rate and the node dormancy strategy are presented.Meanwhile,A multiple target localization method via CS reconstruction theory based on discrete glowworm swarm optimization algorithm(DGSO)is proposed in WSNs.And the improved CS is applied to the multiple target localization in WSNs,and WSNs application model is established.The reasonable measure matrix and sparse matrix is constructed.Finally,the multiple target localization is achieved in WSNs..(4)Sink mobility based and energy balancing unequal clustering protocol(SEBUCP)is presented.By using the improved SFLA(shuffled frog leaping algorithm),SEBUCP chooses the nodes having more energy as cluster heads and divides all nodes into clusters of different size.Competition mechanism between cluster heads is introduced,which is helpful to form a more rational topology.Meanwhile,according to the characteristics of discrete optimization problems,this algorithm put forward the improved discrete shuffled frog leaping algorithm.As the characteristics of the discrete optimization problem are analyzed,the frog encoding of universal significance is defined,which is important for DSFLA to solute discrete optimization problem effectively.Update mechanism based on "coded bits swapped" for DSFLA is designed,and an adaptive weighting factors and sub-ethnic dual strategy are also presented.Furthermore,the improved DSFLA is used in compressed sensing reconstruction algorithm,and the unknown reconstructed signal encoding is understood as frogs' code.