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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:71359", author = "Julien Romieux and Fabio Verdicchio", title = "Cooperative Sensing for Wireless Sensor Networks", 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.", volume = "9", number = "8", pages = "995-5", }