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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maskery, M. Krishnamurthy, V. |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of British Columbia, Vancouver (Maskery, M.; Krishnamurthy, V.) |
| Abstract | We describe a decentralized learning-based activation algorithm for a ZigBee-enabled unattended ground sensor network. Sensor nodes learn to monitor their environment in a low-power "sleep" mode, until an intruder is detected, then enter a full-power mode only if the benefit for doing so outweighs an energy cost. Our formulation accounts for the energy required to transmit and the probability of successful transmission in a crowded ZigBee network. Since these depend on the activity of other nodes, we propose a decentralized adaptive algorithm for sensor activation based on game theoretic principles. We show that the algorithm tracks the time-varying set of correlated equilibria of the problem, and illustrate performance through simulation. The algorithm is described as a stochastic approximation, with attendant differential inclusion analysis. |
| Starting Page | 3915 |
| Ending Page | 3920 |
| File Size | 568398 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424403537 |
| DOI | 10.1109/ICC.2007.645 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-24 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Game theory Peer to peer computing Sensor phenomena and characterization Algorithm design and analysis Target tracking Costs ZigBee Approximation algorithms Stochastic processes Convergence |
| Content Type | Text |
| Resource Type | Article |
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