传感器网络中基于时空相关性的压缩感知算法

Compressed sensing algorithm based on temporal and spatial correlation in Wireless Sensor Networks

摘要:

传感器网络节点的能量有限，为节省传感器节点的能耗，提出了利用节点内及节点间的时空相关性的压缩感知模型及算法，减少了通信的数据量，进一步节省了能耗，延长了网络的生命周期。算法在分簇协议和多跳路由优化的基础上，在簇头节点运用较为简单的压缩感知压缩测量方法，降低了计算复杂度。通过对实测数据的误差分析及能耗仿真，验证了该模型及算法的有效性和实用性。

Abstract:

The compressed sensing model and algorithm adopting the temporal and spatial correlation of intra-node and inter-node are employed in order to cut the energy consumption of sensor nodes. The data amount of communication can be reduced, therefore, the energy consumption can be saved, and the life cycle of the network can be extended. Relatively simple compressed sensing algorithm based on clustering protocol and multi-hop routing optimization at the cluster head node can reduce the calculation complexity. The matrix vector of function and Kronecker product are used for data processing, thus reduces the complexity. The effectiveness and practicality of the model and algorithm are verified by the error analysis on the measured data and the energy consumption simulation.