参与式感知系统中基于压缩感知的数据采集算法

基于压缩感知理论提出了一种在参与式感知系统中进行数据采集的算法.该算法通过对节点社会关系的分析,估计得出部分未被传输的节点感知数据,在此基础上对观测矩阵进行更新,使压缩感知算法可以利用已传输的数据和估计得出的数据进行重构.该算法能显著减少参与式感知系统中传输的数据量,同时能够保证较好的数据重构精度.采用随机漫步移动模型进行了仿真实验,验证了算法的可行性.实验表明,与传统的压缩感知算法相比,上述算法在重构成功率相同的情况下,可以显著减少网络传输的数据量,从而降低网络消耗.

Compressed Sensing Based Data Acquisition Algorithm in Participatory Sensing System

A data acquisition algorithm in participatory sensing systems based on the compressed sensing theory was proposed. In this algorithm， the un-transmitted data was estimated by analyzing social relationship between mobile nodes. And then the observation matrices were refreshed using estimated data. Finally the compressed sensing algorithm was exploited to reconstruct original data according to both transmitted and estimated data. The proposed algorithm could greatly reduce the amount of data transmitted in the participatory sensing systems while still achieve good data reconstruction accuracy. The random walk mobility model was exploited in the simulations to validate the feasibility of this algorithm. The simulation results showed that， compared with the traditional compressed sensing algorithm， the amount of data transmitted over the network could be remarkably reduced without losing data fidelity， and hence the network overhead could be decreased.