Wireless Sensing with Power Constraints
We introduce two conceptual models for wireless sensing and control with power-limited sensors and controllers. The limited battery power of the wireless device is captured in the models by imposing hard constraints on either the number of available transmissions the device can make, or on the number of cycles it can stay awake. Such hard constraints can be viewed as a measurement budget, under which estimation or control policies will have to be developed over a given decision horizon. Among the two representative models studied here, the first one is one of optimal scheduling of a finite measurement budget for a Gauss-Markov process over an observation horizon. The second one is an optimal estimation problem where the number of transmissions the wireless sensor can make is limited to a number, M, which is less than the observation horizon, N. It is shown that both problems can be solved by employing dynamic-programming type arguments, and their solutions have a threshold characterization.
KeywordsWireless Sensing and Control Optimal Scheduling Power-Limited Estimation Dynamic Programming Threshold Policies
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- 1.Industrial wireless technology for the 21st century, based on views of the industrial wireless community, in collaboration with U.S. Department of Energy (DOE), (2002) (available on the DOE’s website at http://energy.gov/industry/sensorsautomation/pdfs/wirelesstechnology.pdf)Google Scholar
- 2.Gutierrez J A, Calllaway E H, Barrett R L (2003) Low-Rate wireless personal area networks: Enabling wireless sensors with IEEE 802.15.4, IEEE Press, New York, NYGoogle Scholar
- 3.Stralen N V, Imer O C, Mitchell R, Evans S, Iyer S (2006) A multiband random access messaging protocol, In: Proceedings of Military Communications Conference (MILCOM), Washington, DCGoogle Scholar
- 4.Azimi-Sadjadi B, Sexton D, Liu P, Mahony M (2006) Interference effect on IEEE 802.15.4 performance, In: Proceedings of 3rd International Conference on Networked Sensing Systems (INNS), Chicago, ILGoogle Scholar
- 5.Imer O C (2005) Optimal estimation and control under communication network constraints, Ph.D. Dissertation, University of Illinois at Urbana-ChampaignGoogle Scholar
- 6.Imer O C, Başar T (2005) Optimal estimation with limited measurements, In: Proceedings of 44th IEEE Conference on Decision and Control (CDC) and European Control Conference (ECC), Seville, SpainGoogle Scholar
- 7.Imer O C, Başar T (2006) Optimal control with limited controls, In: Proceedings of American Control Conference (ACC), Minneapolis, MNGoogle Scholar