Open Access

An Optimal Medium Access Control with Partial Observations for Sensor Networks

EURASIP Journal on Wireless Communications and Networking20052005:487028

https://doi.org/10.1155/WCN.2005.505

Received: 10 December 2004

Published: 8 September 2005

Abstract

We consider medium access control (MAC) in multihop sensor networks, where only partial information about the shared medium is available to the transmitter. We model our setting as a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain representing the available bandwidth, and in which the arrivals are controlled based on the partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state on which the control decisions are based. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results can be specifically applied for designing efficient and stable algorithms for medium access control in multiple-accessed systems, in particular for sensor networks.

Keywords

MAC feedback control controlled Markov chains Markov decision processes dynamic programming stochastic stability

Authors’ Affiliations

(1)
Center for the Mathematics of Information, California Institute of Technology, Caltech
(2)
School of Electrical and Computer Engineering, College of Engineering, Cornell University

Copyright

© R. Cristescu and S. D. Servetto 2005

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.