Global Correlated Data Gathering in Wireless Sensor Networks with Compressive Sensing and Randomized Gossiping
In this paper, we address the problem of access to global information from any single point in wireless sensor networks. To this end, we propose a distributed in-network data acquisition approach, on the basis of compressive sensing, in which sparse random projections and randomized gossiping are jointly designed. In the context of unreliable distributed wireless settings, a simple random gossip algorithm is adopted. It allows the sensor to forward the linear combined data (readings) to randomly chosen neighbors. Also, each transmission path of the combined data is mapped to one row of the projection matrix, forming low-cost sparse random projections. Moreover, a theoretical model is developed that exactly characterizes the relationship of the number of sparse projections, the degree of the sparsity, and the error probability. Finally, simulation results show that, compared with the conventional approach, the proposed algorithm can achieve better reconstruction quality with less communication overhead.