In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.
References
A. Ribeiro and G. Giannakis, “Bandwidth-constrained distributed estimation for wireless sensor networks, Part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp. 1131–1143, Mar. 2006.
D. P. Spanos, R. Olfati-Saber, and R. M. Murray, “Dynamic consensus on mobile networks,” in Proc. 16th IFAC World Congr., Prague, Czech Republic, Jul. 2005.
I. D. Schizas, A. Ribeiro, and G. B. Giannakis, “Consensus in ad hoc wsns with noisy links – Part I: Distributed estimation of deterministic signals,” IEEE Trans. Signal Process., vol. 56, no. 1, pp. 350–364, Jan.2008.
J.-J. Xiao and Z.-Q.Luo, “Decentralized estimation in an inhomogeneous sensing environment,” IEEE Trans. Inf. Theory, vol. 51, pp. 3564–3575, Oct. 2005.
J.-J. Xiao, A. Ribeiro, Z.-Q.Luo, and G. B. Giannakis, “Distributed compression-estimation using wireless sensor networks,” IEEE Signal Process. Mag., vol. 23, no. 4, pp. 27–41, Jul. 2006.
J. Li and G. AlRegib, “Rate-constrained distributed estimation in wireless sensor networks,” IEEE Trans. Signal Process., vol. 55, no. 5, pp.1634–1643, May 2007.
S.Kumar, F. Zhao, and D. Shepherd, “Special issue on collaborative information processing in microsensor networks,” IEEE Signal Process. Mag., vol. 19, no. 2, pp. 13–14, Mar. 2002.
T. C. Aysal and K. E. Barner, “Constrained decentralized estimation over noisy channels for sensor networks,” IEEE Trans. Signal Process., vol. 56, no. 4, pp. 1398–1410, Apr. 2008.
T. C. Aysal and K. E. Barner, “Blind decentralized estimation for bandwidth constrained wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 7, no. 5, pp. 1466–1471, May 2008.
W. L.Winston and M. Venkataramanan, Introduction to MathematicalProgramming, 4th ed. Pacific Grove, CA: Duxbury Press, 2003.
Z.-Q. Luo, “Universal decentralized estimation in a bandwidthconstrained sensor network,” IEEE Trans. Inf. Theory, vol. 51, pp. 2210–2219, Jun. 2005.