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Author Yuan, Zhaohui ♦ Xing, Guoliang ♦ Chen, Yixin
Source CiteSeerX
Content type Text
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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract 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 non-linear optimization problem with prohibitive 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 7-fold speedup over the optimal algorithm. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study