The Impact of Transmit Rate Control on Energy-Efficient Estimation in Wireless Sensor Networks

We study the impact of physical layer (PHY) transmit rate control on energy efficient estimation in wireless sensor networks. A sensor network collects measurements about an unknown evolving process. Each sensor controls its sampling rate and its PHY transmit rate to the next hop or to the Fusion Center (FC). The FC performs estimation of the unknown process based on sensor measurements and needs to adhere to an estimation accuracy constraint. The objective is to maximize sensor network lifetime. The tradeoff is that, high PHY transmit rates consume more energy per transmitted bit, but they increase the amount of transmitted sensor measurement data per unit time, and thus they aid in improving estimation quality and in satisfying the estimation error constraint. First, we study a single-hop network where sensors transmit directly to the FC. In this case, sensor sampling rates are directly mapped onto PHY transmit rates. We identify fundamental structural properties of the optimal solution, and we propose a distributed, iterative sensor PHY rate adaptation algorithm for reaching a solution, based on light-weight feedback from the FC. Next, we consider the multi-hop version of the problem, where the sensor measurement (sampling) rates, PHY transmit rates and data flows to the FC are controlled. We extend the distributed optimization framework above to include all controllable parameters, and we devise an iterative algorithm for maximizing network lifetime.

[1]  Andrea J. Goldsmith,et al.  Modeling and optimization of transmission schemes in energy-constrained wireless sensor networks , 2007, TNET.

[2]  Junlin Li,et al.  Distributed estimation in resource-constrained wireless sensor networks , 2008 .

[3]  Vikrant Sharma,et al.  Improving lifetime data gathering and distortion for mobile sensing networks , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[4]  Slawomir Stanczak,et al.  Transmit Rate Control for Energy-Efficient Estimation in Wireless Sensor Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[5]  Joongseok Park,et al.  Maximum Lifetime Routing In Wireless Sensor Networks ∗ , 2005 .

[6]  穂鷹 良介 Non-Linear Programming の計算法について , 1963 .

[7]  Andrea J. Goldsmith,et al.  Energy-constrained modulation optimization , 2005, IEEE Transactions on Wireless Communications.

[8]  Gregory J. Pottie,et al.  On sensor network lifetime and data distortion , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[9]  Mani B. Srivastava,et al.  Power management for energy-aware communication systems , 2003, TECS.

[10]  Andrea J. Goldsmith,et al.  Estimation Diversity and Energy Efficiency in Distributed Sensing , 2007, IEEE Transactions on Signal Processing.

[11]  Prasun Sinha,et al.  Joint Energy Management and Resource Allocation in Rechargeable Sensor Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[12]  Andrea J. Goldsmith,et al.  Cross-Layer Energy and Delay Optimization in Small-Scale Sensor Networks , 2007, IEEE Transactions on Wireless Communications.

[13]  Zhi-Quan Luo,et al.  Minimum Energy Decentralized Estimation in a Wireless Sensor Network with Correlated Sensor Noises , 2005, EURASIP J. Wirel. Commun. Netw..

[14]  Xiaoxin Qiu,et al.  On the performance of adaptive modulation in cellular systems , 1999, IEEE Trans. Commun..

[15]  Iordanis Koutsopoulos,et al.  Lifetime Maximization in Wireless Sensor Networks with an Estimation Mission , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.