Energy-Efficient Data Acquisition in Wireless Sensor Networks Using Compressed Sensing

In this paper, we study the problem of data acquisition in wireless sensor networks (WSNs). A recently revitalized technique called compressive sensing (CS) has presented a new method to capture sparse signals at a rate below Nyquist. There are drawbacks to directly applying the existing CS algorithm to WSNs, which are mainly due to the fact that CS requires a large number of inter-communications for generating each projection. To mitigate these drawbacks, we propose compressive distributed sensing using random walk (CDS(RW)), an algorithm for CS in WSNs that uses rate less coding. This algorithm is independent of routing algorithms and network topologies. CDS(RW) collects sufficient number of sensor readings while combining them together without significantly increasing the inter-communication cost. We model the CS problem with code design for a set of parallel channels which helps us to design the rate less code degree distribution. This model provides the advantage of using non-uniform and unequal error protection codes.