Spatio-temporal Compression Based Clustering Optimization Scheme In Wireless Sensor Networks

Wireless Sensor Network(WSN)currently as distributed network is mainly used for data sensing,processing and transmitting of various monitoring objects in the monitoring area.Frequent data sampling result in a large amount of redundant data among the original data in the network,which will induce lots of consumption on limited network energy,storage space and network bandwidth.Based on above factors,how to collect the sensory data efficiently with lower energy consumption has become a great challenge,especially for large-scale network.In this dissertation,we explore the spatio-temporal correlations of sensory data,combine the theory of compressed sensing(CS),clustering ideas and optimization methods.The main contributions of this dissertation include the following three aspects.1)Data spatial compression scheme based on LEACH.The proposed scheme can prolong the lifetime of WSNs effectively by selecting the cluster heads alternately for balancing the energy consumption.Meanwhile,this scheme can explore spatial correlations by utilizing the CS,which can improve the lifetime by reducing the data transmission.Finally,the simulation results confirm that the lifetime of the proposed scheme is longer than other related schemes,and the original data can be reconstructed with high precision.2)Spatial compression based selection algorithm for cluster heads.The proposed algorithm can search the optimal location of the cluster head by defining several fitness functions,which can control the size of cluster and obtain a reasonable distribution of cluster head.Meanwhile,considering the advantages of high computing power and unrestricted energy of sink node,the selection algorithm of cluster heads is performed in the sink node and the election results will be broadcasted to the whole network.CS is used for spatial compression of sensory data in the phase of data communications,which can reduce the transmission of redundant data effectively.The simulation results show that the developed algorithm obtains obvious advantages on the energy consumption and lifetime.3)Spatio-temporal compression based dynamic optimization scheme for cluster head number.According to the total number of sensory nodes in WSN,the proposed scheme can effectively control the distribution and scale of clusters by adaptive clustering.Meanwhile,the constructed energy and distance model based optimization algorithm optimizes the cluster heads number of each cluster dynamically,which can reduce the energy consumption of each cluster and improve the self-organization and self-adaptation of network.Moreover,the CS is used to explore the spatio-temporal correlations of sensory data,and the spatio-temporal compression is achieved in cluster head and sensory nodes,as a result,it decreases the amount of data transmission.Finally,the simulation results show that the proposed scheme reduces the energy consumption as compared with other related schemes when it obtains high reconstruction precision,and further extends the lifetime of whole data collection network.