Energy Optimization Strategies For Wireless Sensor Networks Based On Matrix Completion Algorithm

Due to the limited power of sensor nodes and the non replaceable nature of the battery,the energy saving optimization problem of wireless sensor networks has become a key issue in the design of the network.Because the data collected by large-scale wireless sensor networks usually share a strong spatial and temporal correlation between them,there will be a large amount of redundant data which wastes a lot of unnecessary network resources during the process of data acquisition and transmission.At present,many energy saving optimization strategies use the compressed sensing algorithm to compress and then transfer the data collected by sensors,and recover the original data at the receiving end,thus reduce the transmission energy consumption of the redundant data.In order to further optimize the energy consumption of the network,this paper put forward to use matrix completion algorithm which is a current research hotspot to reduce both the energy consumption of the sensor network acquisition and transmission process and achieve the purpose of prolonging the network lifetime.In this paper,two steps of energy saving optimization algorithm are proposed.Firstly,we put forward a node selection strategy based on the matrix completion algorithm.Through the measurement of the amount of information of the sensor node's data,we find out the non-redundant nodes and let these nodes implement the data acquisition function.We will close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes.Thus,the amount of data collected and transmitted by the network are both reduced,and the total energy consumed by the acquisition module and the transmission module of the network are also reduced.Secondly,we take advantage of the partial sampling scheme in matrix completion algorithm to directly reduce the amount of data collected in the sampling phase by the non-redundant nodes in the first step and transmit the partial sampling data in the transmission stage,which eliminates the overall sampling and the compression process during compressed sensing algorithm.Thereby,we can reduce the energy consumption on both data collection and transmission process of the wireless sensor network.Our paper utilized the meteorological data sets collected by the sensor network deployed by The Swiss Federal Institute of technology in Lausanne to perform the proposed two-step energy optimization algorithm.The experiment results show that under a certain recovery precision,the algorithm effectively reduces the number of redundant nodes in the network,and achieves the purpose of reconstructing the original data with the least amount of data collection and transmission.Through the establishment of sensor network energy consumption model,this paper calculates the energy consumption of the optimized sensor network which only consumes 17.8%of the energy consumed before optimization.The optimization shows important practical significance for the energy saving problem of wireless sensor networks.In addition,this paper makes optimization to the matrix completion algorithm to accelerate the convergence rate of the algorithm and make it more suitable for dealing with the high dimensional data in large scale wireless sensor networks.