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Issue No.08 - August (2011 vol.22)
pp: 1407-1414
Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.
wireless sensor networks, concave programming, nonlinear programming, object detection, probability, sensor fusion, sensor placement, probabilistic data fusion model, fusion-based surveillance networks, mission-critical target detection, wireless sensor networks, detection probability, false alarm rates, optimal sensor placement, nonlinear optimization problem, nonconvex optimization problem, computational complexity, Surveillance, Sensors, Data models, Object detection, Energy measurement, Noise, Noise measurement, wireless sensor networks., Data fusion, target detection, sensor placement
"Sensor Placement Algorithms for Fusion-Based Surveillance Networks", IEEE Transactions on Parallel & Distributed Systems, vol.22, no. 8, pp. 1407-1414, August 2011, doi:10.1109/TPDS.2011.115
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