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

Qiang Niu | Zendy; Tian Huan | Zendy; Pengpeng Chen | Zendy

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

Author(s) -

Qiang Niu,

Tian Huan,

Pengpeng Chen

Publication year - 2016

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2016/7061486

Subject(s) - computer science , tracking (education) , monte carlo method , algorithm , range (aeronautics) , wireless sensor network , monte carlo algorithm , data mining , psychology , computer network , pedagogy , statistics , materials science , mathematics , composite material

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.

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