Abstract
In this paper, we provide a stochastic adaptive sampling strategy for mobile sensor networks to estimate scalar fields over a surveillance region using kernel regression. Our approach builds on a Markov Chain Monte Carlo (MCMC) algorithm particularly known as the Fastest Mixing Markov Chain (FMMC) under a quantized finite state space for generating the optimal sampling probability distribution asymptotically. An adaptive sampling algorithm for multiple mobile sensors is designed and numerically evaluated under a complicated scalar field. The comparison simulation study with a random walk benchmark strategy demonstrates the good performance of the proposed scheme.
| Original language | English |
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| Title of host publication | Proceedings of the 2010 American Control Conference, ACC 2010 |
| Pages | 2897-2902 |
| Number of pages | 6 |
| Publication status | Published - 2010 Oct 15 |
| Event | 2010 American Control Conference, ACC 2010 - Baltimore, MD, United States Duration: 2010 Jun 30 → 2010 Jul 2 |
Publication series
| Name | Proceedings of the 2010 American Control Conference, ACC 2010 |
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Other
| Other | 2010 American Control Conference, ACC 2010 |
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| Country | United States |
| City | Baltimore, MD |
| Period | 10/6/30 → 10/7/2 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
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Cite this
Xu, Y., & Choi, J. (2010). Stochastic adaptive sampling for mobile sensor networks using kernel regression. In Proceedings of the 2010 American Control Conference, ACC 2010 (pp. 2897-2902). [5531511] (Proceedings of the 2010 American Control Conference, ACC 2010).