基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法  

Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing

作　　者：周颖[1] 杨丽花[1] 杨龙祥[1] 倪梦 ZHOU Ying;YANG Lihua;YANG Longxiang;NI Meng Nanjing(University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学,江苏南京210003

出　　处：《电信科学》2019年第1期74-80,共7页Telecommunications Science

基　　金：江苏省高等学校自然科学研究项目(No.18KJ13510034);第11批中国博士后科学基金资助项目(No.2018T110530);国家自然科学基金资助项目(No.61401232;No.61427801;No.61671251;No.61372124;No.61501254);江苏省自然科学基金资助项目(No.BK20140894)~~

摘　　要：提出了一种联合双重卡尔曼滤波与混合压缩感知的数据收集方案。在簇内传输时,簇内成员节点利用卡尔曼滤波预测选择性地向簇头发送数据,以减少数据传输量;同时,簇间传输时,在多跳传输的过程中利用混合压缩感知技术进行数据压缩,提高系统的能效。仿真结果表明,与现有方案相比,新算法能够在保证汇聚节点重构精度的前提下,显著地降低数据收集的能耗。A novel Kalman prediction and compressed sensing based energy-efficient data gathering scheme was proposed.Specially,in the intra-cluster transmission,the cluster members utilized the Kalman prediction to selectively send the data to their cluster heads.In the inter-cluster transmission,the cluster heads leveraged the hybrid compressed sensing to transfer the data to the sink via multi-hop links.Moreover,the communication cost was derived to verify the efficiency of the proposed method.Simulation results show that the proposed method has higher energy efficiency compared with the available schemes,and the sink can obtain measurements with reasonable quality by using the proposed method.

关 键 词：无线传感器网络 卡尔曼预测 压缩感知 时空相关性 能效 

分 类 号：TN929[电子电信—通信与信息系统]