Compressible Data Fusion Based on Minimum Energy Consumption in Wireless Sensor Network

Abstract

Data fusion problem is one of the research hotspots in wireless sensor network. Aiming at the shortage of excessive energy consumption in existing fusion method, a data fusion program is proposed based on minimum energy consumption according to the theory of compressive sensing. This paper first analyzes the impact of different fusion modes on data collection performance, considers the impact of routing and mixed Compressive Sensing (CS) fusion on energy optimization, models the data fusion problem as the mixed integer programming problem based on minimum energy consumption, and proposes a greedy algorithm which is growing based on fusion node to solve the problem. The algorithm divides all nodes in the network into fusion nodes and forwarding nodes. Fusion node is used to conduct CS coding, while forwarding node is used to forward its own data and the data received, so as to obtain a data fusion tree with minimum energy consumption. The simulation results show that the proposed program is effective, because it is better than traditional methods in data reconstruction accuracy, network lifetime and other aspects.