Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

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).


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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).

Item Type: Article
Keywords: Algorithms, Management, Measurement, Adaptive sampling algorithm, Decentralised decision mechanism, Gaussian process regression, Information metric.
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 266579
Accepted Date and Publication Date:
March 2009Submitted
Date Deposited: 19 Aug 2008 11:01
Last Modified: 31 Mar 2016 14:13
Further Information:Google Scholar

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