期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 62, 期 2, 页码 293-303出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2012.2214952
关键词
Digital elevation models; evolutionary computation; geographic information systems; optimization; wireless sensor networks
资金
- GEOmatics for Informed DEcisions Network of Centres of Excellence (Canada)
- Defence Research and Development Canada
- MDA Systems Ltd.
This paper proposes a probabilistic sensor model for the optimization of sensor placement. Traditional schemes rely on simple sensor behaviour and environmental factors. The consequences of these oversimplifications are unrealistic simulation of sensor performance and, thus, suboptimal sensor placement. In this paper, we develop a novel probabilistic sensing model for sensors with line-of-sight-based coverage (e.g., cameras) to tackle the sensor placement problem for these sensors. The probabilistic sensing model consists of membership functions for sensing range and sensing angle, which takes into consideration sensing capacity probability as well as critical environmental factors such as terrain topography. We then implement several optimization schemes for sensor placement optimization, including simulated annealing, limited-memory Broyden-Fletcher-Goldfarb-Shanno method, and covariance matrix adaptation evolution strategy.
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