Cooperative Sensing for Wireless Sensor Networks

Authors: Julien Romieux, Fabio Verdicchio

Abstract:

Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: Cooperative signal processing, power management, signal representation, signal approximation, wireless sensor networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109962

References:

[1] M. N. Halgamuge, M. Zukerman, K. Ramamohanarao and H. L. Vu, "An estimation of sensor energy consumption," Progress in Electromagnetics Research B, vol. 12, pp. 259-295, 2009.
[2] Fenxiong Chen, Yaodong Shen, Jun Liu and Fei Wen, "Nonthresholdbased node level algorithm of data compression over the wireless sensor networks," in Signal Processing Systems (ICSPS), 2010 2nd International Conference on, 2010, pp. V2-223-V2-227.
[3] F. Chen, F. Lim, O. Abari, A. Chandrakasan and V. Stojanovic, "Energy-Aware Design of Compressed Sensing Systems for Wireless Sensors Under Performance and Reliability Constraints," Circuits and Systems I: Regular Papers, IEEE Transactions on, vol. 60, pp. 650-661, 2013.
[4] A. Jindal and K. Psounis, "Modeling spatially-correlated sensor network data," in Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on, 2004, pp. 162-171.
[5] J. Z. Sun and V. K. Goyal, "Intersensor Collaboration in Distributed Quan-tization Networks," Communications, IEEE Transactions on, vol. 61, pp. 3931-3942, 2013.
[6] M. G. Rabbat and R. D. Nowak, "Quantized incremental algorithms for distributed optimization," Selected Areas in Communications, IEEE Journal on, vol. 23, pp. 798-808, 2005.
[7] G. Rajesh, B. Vinayagasundaram and G. S. Moorthy, "Data fusion in wireless sensor network using simpson's 3/8 rule," in Recent Trends in Information Technology (ICRTIT), 2014 International Conference on, 2014, pp. 1-5.
[8] Wei Chen, M. R. D. Rodrigues and I. J. Wassell, "A Frechet Mean Approach for Compressive Sensing Data Acquisition and Reconstruction in Wireless Sensor Networks," Wireless Communications, IEEE Transactions on, vol. 11, pp. 3598-3606, 2012.
[9] C. R. Rao, Handbook of Statistics 9: Computational Statistics. North- Holland, 1993.