Uncertainty-aware and coverage-oriented deployment for sensor networks
Journal of Parallel and Distributed Computing
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Sensor Placement Algorithms for Fusion-Based Target Detection
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
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In this paper, we study the sensor deployment problem in a value fusion based distributed sensor network (DSN) detection system. More specifically, we study the problem of determining the positions at which a fixed number of sensors can be deployed in order to minimize the squared error (SE) between achieved and required detection probabilities while satisfying false alarm requirements. We show that this deployment problem can be modeled as a linear quadratic regulator problem (LQR). Subsequently, we develop two deployment algorithms; an optimal control based and a suboptimal deployment algorithm. We compare the performance of the proposed algorithms to that of a greedy deployment algorithm. Results indicate that the proposed algorithms have a faster SE convergence rate than that of the greedy algorithm. As a result, the proposed algorithms can use as much as 25% fewer number of sensors than the greedy algorithm to satisfy the same detection and false alarm requirements.