摘要
压缩感知(compressive sensing,CS)具有减少数据量和能量负载均衡的特点,提供了利用少量测量值恢复原始数据的新方法,使得数据收集的能量消耗大大减少。针对无线传感器网络寿命最大化进行研究,将混合压缩感知算法与分簇算法结合,基站从N个传感器收集M个测量向量,利用压缩感知高概率的恢复N传感器收集的数据,极大地减少了网络能量的消耗。在簇内,簇头节点收集簇内节点的数据,然后对数据压缩进行处理,将自己本身的数据投影后,两者数据相加,簇头间建立骨干网,簇头沿骨干网数据传输数据至父簇头或基站。进一步,分析了网络的能量消耗和能量消耗最少时的最优簇数量的关系,最后,通过实验仿真,提出的算法和已经存在的算法相比能提高网络寿命。
Since compressive sensing（CS） provides a novel number of measurements, the energy consumption for data gathering in WSNs is reduced significantly. This paper investigated the application of CS to data collection in wireless sensor networks and aimed at minimizing the network energy consumption through joint routing and compressed sensing, the sink collected the M projections from N sensors. In the cluster,the common sensors sent their data to the cluster head directly, the cluster head aggregated the received data using CS and forward them to the sink or father cluster head via a backbone routing tree. Furthermore, the paper analyzed the relationship between the network energy consumption and cluster size. The simulation results show that the proposed algorithm is effective, and is better than other existed algorithm in terms of energy consumption.
出处
《计算机应用研究》
CSCD
北大核心
2015年第12期3756-3759,共4页
Application Research of Computers
基金
国家重大设备开发专项资金资助项目(2013YQ030595)
关键词
无线传感器网络
压缩感知
骨干网
分簇
网络寿命
wireless sensor networks
compressive sensing
backbone
cluster
network life
