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Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

Record type: Article

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute).

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Citation

Kho, Johnsen, Rogers, Alex and Jennings, Nick (2009) Decentralised Control of Adaptive Sampling in Wireless Sensor Networks ACM Transactions on Sensor Networks, 5, (3), article 19-(35 pages).

More information

Submitted date: March 2009
Published date: 2009
Keywords: Algorithms, Management, Measurement, Adaptive sampling algorithm, Decentralised decision mechanism, Gaussian process regression, Information metric.
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 266579
URI: http://eprints.soton.ac.uk/id/eprint/266579
PURE UUID: b2fa9aed-3614-48a4-a89e-ffc9f9cb158c

Catalogue record

Date deposited: 19 Aug 2008 11:01
Last modified: 18 Jul 2017 07:15

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Contributors

Author: Johnsen Kho
Author: Alex Rogers
Author: Nick Jennings

University divisions


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