A neural-network-based context-aware handoff algorithm for multimedia computing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper presents a restricted-area-based localization algorithm (RAL) for wireless sensor networks (WSN), in which radio connectivity and principle of perpendicular bisectors are used to provide a lower estimation error than some of restricted-area-based localization algorithms. In the RAL algorithm, anchor nodes can transmit beacon signals at different power levels, which divide the possible transmission ranges of an anchor into a circle and multiple rings. The intersection of circle or rings of all the anchors heard by unknown node forms restricted-area I. In addition, we utilize all the perpendicular bisectors of the line which connects each pair of anchor nodes to obtain restricted-area II. Based on the restricted-area I and restricted-area II, we can calculate valid intersection points, and take average value of all these points as the estimated location of the unknown nodes. The proposed algorithm is range-free and energy efficient. Neighboring sensor nodes do not need to exchange information. Each sensor node only relies on information of anchors it heard to compute two kinds of restricted-areas and estimates its location. Simulation results show that the proposed algorithm has less estimation error than Centroid, Convex and CAB localization algorithms.