engleski

RSS-based Localization in Wireless Sensor Networks using SOCP Relaxation

This paper addresses the problem of locating a single source from noisy received signal-strength (RSS)measurements in wireless sensor networks (WSNs). To overcome the non-convexity of the maximum likelihood (ML) optimization problem, we provide an efficient convex relaxation that is based on the second order cone programming (SOCP), for both cases of known and unknown source transmit power, and we use a simple iterative procedure to solve the problem when the transmit power and the path loss exponent (PLE) are simultaneously unknown. Simulation results demonstrate that the new approach outperforms the existing ones in terms of the estimation accuracy, while in terms of the complexity, it represents a good balance when compared to the existing approaches.

wireless sensor networks; optimization; cognitive radio; localization

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