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A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields

Muhammad F Mysorewala1*, Lahouari Cheded1 and Dan O Popa2

Author Affiliations

1 Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

2 Electrical Engineering Department, The University of Texas at Arlington, Arlington, TX, 76019, USA

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EURASIP Journal on Wireless Communications and Networking 2012, 2012:223  doi:10.1186/1687-1499-2012-223

Published: 18 July 2012


Monitoring widespread environmental fields is undoubtedly a practically important area of research with many complex and challenging tasks. It involves the building of models of the fields or natural phenomena to be monitored, the estimation of the spatio-temporal distribution of a variety of environmental parameters of interest, such as moisture or salinity in a crop field, or the spatial distribution of vital natural resources such as oil and gas, etc. Sampling, a key operation of the monitoring process, is a broad methodology for gathering statistical information about the phenomenon, or environmental variable, being monitored. To efficiently monitor widespread fields and estimate the spatio-temporal distribution of some particular environmental variable, calls for the use of a sampling strategy can fuse information from different scales of sensors. Such an attractive strategy is well catered for by both the capabilities and distributed nature of wireless sensor networks and the mobility of robots performing the sampling (sensing) tasks. This sampling strategy could even be rendered “adaptive” in that the decision of “where to sample next” evolves temporally with past measurements and is optimally computed. In this article, we examine various single-robot and multi-robot adaptive sampling schemes based on different extended Kalman filter filtering structures such as centralized and decentralized filters as well as our own novel decentralized and distributed filters. Our investigation shows that, whereas the first two filters suffer from a heavy computational or communication load, our proposed method, through its key feature of distributing the filtering task amongst the robots used, manages to reduce both loads and the total reconstruction time. It also enjoys the added attractive feature of scalability that allows the structure of the proposed monitoring scheme to grow with the complexity of the field under study. Our results are corroborated by our simulation work and offer ample encouragement for a further theoretical investigation of some properties of the proposed scheme and its implementation on a physical system. Both of these activities are currently underway.

Spatial field estimation; Adaptive sampling; Information fusion; Multi-scale sensing; Robotic sensor network; Environmental monitoring