Introduction to algorithms
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Compressive data gathering for large-scale wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Energy-Efficient Data Acquisition in Wireless Sensor Networks Using Compressed Sensing
DCC '11 Proceedings of the 2011 Data Compression Conference
Improving Network Lifetime for Wireless Sensor Network Using Compressive Sensing
HPCC '11 Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications
IEEE Transactions on Information Theory
PECA: Power Efficient Clustering Algorithm for Wireless Sensor Networks
International Journal of Information Technology and Web Engineering
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This paper proposes a novel data gathering method using Compressive Sensing (CS) and random projection to improve the lifetime of large Wireless Sensor Networks (WSNs). To increase the network lifetime, one needs to decrease the overall network energy consumption and distribute the energy load more evenly throughout the network. By using compressive sensing in data aggregation, referred to as Compressive Data Gathering (CDG), one can dramatically improve the energy efficiency, and this is particularly attributed to the benefits obtained from data compression. Random projection, together with compressive data gathering, helps further in balancing the energy consumption load throughout the network. In this paper, we propose a new compressive data gathering method called Minimum Spanning Tree Projection (MSTP). MSTP creates a number of Minimum-Spanning-Trees (MSTs), each rooted at a randomly selected projection node, which in turn aggregates sensed data from sensors using compressive sensing. We compare through simulations our method with the existing data gathering schemes. We further extend our method and introduce eMSTP, which joins the sink node to each MST and makes the sink node as the root for each tree. Our simulation results show that MSTP and eMSTP outperform the existing data gathering schemes in decreasing the communication cost and distributing the energy consumption loads and hence improving the overall lifetime of the network.