Research On The Data Aggregation Technologies For M2M Wireless Network

M2M(Machine to Machine),whose purpose is to enhance the communication ability of machine and achieve the goal of interconnection of the world,has become a hot research field in communication area With the development of internet of things related technologies.Wireless Sensor Networks(WSNs),on behalf of the area network,have been widely used because of its convenience and inexpensive features.WSNs provides underlying network support for M2M system and promote the applications of M2M.However,due to cost and environmental constraints,these nodes in the network are usually energy and storage limited,how to use limited resources to exrend network lifetime become an important research direction.As data transmission will consume a lot of energy in wireless network,data aggregation technology mainly use local computing and storage capacity to compress or aggregate network data.The goal of data aggregation is to reduce energy consumption brought by data transmission and prolongthe network life time.Research on data aggregation include aggregation structure design,timing in data aggregation and data processing algorithm.Based on the resource-constrained scenario of WSNs,the main work of this paper is about aggregation structure design and data processing.On the basis of research on existing technology,this paper presents Data Aggregation Oriented Clustering(DAOC)and Spatial-Temporal Correlation based in-Cluster Data Aggregation(STCCDA).DAOC build a stable structure for the network firstly.Through non-uniform cluster head selection,local competition among cluster heads and cluster adaptively merge or split,DAOC divides the network into clusters.DAOC makes the size and positon of every cluster adapt with each other.Thus,enrgy comsumption in the network become more balance and the dead time of each node has been prolonged.Comparative simulation verifies the validity of DAOC.As the data collected by nodes has temporal and spatial correlation,On the basis of cluste structure,STCCDA aggragate data on the cluster member nodes and cluster head separately.By building linear prediction function,the cluster member node suppress data transmission and reduce intra-cluster communication consumption while meeting the application needs.On the cluster head,STCCDA takes advantage of compressive sensing and joint sparse model and presents an improved data compression measurement method.STCCDA can both reduce computing,storage costs and reduce the amout of data transferred buy cluster head.The simulation analysis proves that STCCDA can effectively reduce inra-cluster and cluster-head communication consumption.