Abstract:
Wireless sensor network (WSN) is an emerging technology with unprecedented opportunities for wide variety of applications in the present world. The essential task in many applications of sensor networks is to extract relevant information about the sensed data and deliver it with a desired fidelity to a central collection point or sink. WSNs, or more specifically each sensor node, are resource constrained. They have limited power supply, bandwidth for communication, processing speed, and memory space which make the reduction of communication critical to increase the network's performance and lifetime. Data compression is one effective method to utilize limited resources of WSNs. Compressive Sensing (CS) is a novel data compression technique that exploits the inherent correlation in the input data to compress it by means of quasi-random matrices. Distributed Compressed Sensing (DCS) is an extension of CS to multiple-signal case. Since sensors presumably observe related phenomena, the ensemble of signals they acquire may be expected to possess some joint structure, or inter-signal correlation, in addition to the intra-signal correlation in each individual sensor's measurements. DCS enables new distributed coding algorithms that exploit both intra- and inter-signal correlation structures.
Also, nodes close to the sink transmit more data and consume more energy than those at the peripheral of the network. The unbalanced energy consumption has a major impact on network lifetime. Compressive data gathering (CDG) leverages compressive sensing (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks by balancing the energy consumption and reducing the transmissions. With the recent developments in DCS reducing the communication costs in sensor networks, we propose Distributed Compressive Data Gathering (DCDG) to further reduce the communication costs in data gathering and number of measurements in WSNs
