Lifetime Extention Technology For Wireless Sensor Network Based On Compressed Sensing

A wireless sensor network(WSN) is an ad hoc wireless communications network consists of sensor nodes in the monitoring area. Wireless sensor networks are data-centric networks, and the nodes cooperatively sense various environmental information, such as temperature, humidity, pollutant concentration, pressure, etc. The sensed information is transmitted to the base station, and then to the user. The main features of wireless sensor networks is severely limited resources, especially for the constrained energy, which directly affects the lifetime of the whole network. Therefore, energy saving problem is of great concern for WSNs to run longer. In this paper, the technology of how to prolong the lifetime of WSN through compressed sensing is studied, the main work is as follows:1) Considering the application of compression sensing in unreliable WSN, a data gathering model is proposed. WSN nodes firstly utilize compressed sensing technology to encode the perception data and then transmit the encoded data packets to the sink node. Energy consumption of the network is deduced based on the data acquisition model of compression perception and the traditional data acquisition model respectively. The comparisons and analysis of the network energy consumption between the two models are presented, the results show that the latter not only reduces the total energy consumption of the network but also balances the energy consumption of a single node. Thereby, extending the network lifetime.2) In the multi-hop WSN, a clustering data gathering model based on compressed sensing technology is proposed. First, in a large WSN whose nodes densely deployed, considering the data sensed by different nodes with spatial correlation and the data sensed by a single node with temporal correlation, the sensed data in the cluster nodes and the cluster head are compressed respectively. Secondly, we give a cluster model which the cluster nodes could communicate with the cluster head by multi-hop, the energy consumption between the nodes in the cluster and the cluster head to transmit the energy sink is analyzed. We clustering base on the lowest energy consumption of the network. Finally, determining the optimal number of clusters and extending the network lifetime.