NMCT: A Novel Monte Carlo-Based Tracking Algorithm Using Potential Proximity Information

Qiang Niu, Tian Huan and Pengpeng Chen

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 2, 7061486

Abstract: Currently, many services benefit greatly from the availability of accurate tracking. Tracking in wireless sensor networks remains a challenging issue. Most tracking methods often require a large number of anchors and do not take advantage of potential localization information, leading to poor accuracy. To solve this problem, this paper proposes a Novel Monte Carlo-based Tracking (NMCT) algorithm with area-based and neighbor-based filtering, which fully extracts the proximity information embedded in the neighborhood sensing. We describe the entire system design in detail and conduct extensive simulations. The results show that the proposed algorithm outperforms the typical schemes under a wide range of conditions.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:2:p:7061486

DOI: 10.1155/2016/7061486

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