WSNs中基于期望网络覆盖和分簇压缩感知的数据收集方案

为提高无线传感器网络数据收集精确度、降低网络能耗和改善数据包丢失情况下数据收集算法的鲁棒性,提出一种基于期望网络覆盖和分簇压缩感知的数据收集方案.首先设计期望网络覆盖优化算法,给出节点调度策略,实现对“特殊”区域重点观测和降低节点能耗的目的;然后通过分析网络分簇与节点部署之间的关系,设计弱相关性观测矩阵,降低数据包丢失对数据收集的影响;最后引入群居蜘蛛优化算法以提高汇聚节点处CS数据重构精度.仿真结果表明,与其他数据收集算法相比,所提出方案数据重构误差降低了约23.5{%

Data collection scheme based on expected network coverage and cluster compressive sensing for WSNs

In order to improve the wireless sensor network(WSN) data collection accuracy, reduce the energy consumption of the network and improve the robustness of data collection algorithm under packet loss condition, a data collection scheme based on expected network coverage and cluster compressive sensing is proposed. The data collection scheme is divided into two steps as expected network coverage optimization and cluster CS (compressive sensing) data collection. Firstly, the expected network coverage optimization algorithm is designed, and the node scheduling strategy is given through the quantitative analysis of the node coverage redundancy and the expected value of network coverage in the key observation area, which helps to achieve the purpose of the ``special'' area observation and reduce energy consumption. Then, by analyzing the relationship between networks clustering and node deployment, the adaptive dynamic network clustering results are provided. On this basis, the weak correlation observation matrix is designed, which can reduce the influence of the packet loss on CS data collection. Finally, the social spider optimization algorithm is introduced to improve the reconstruction accuracy of the CS. The simulation results show that compared with other data collection algorithms, the data reconstruction error is reduced by about 23.5{%