Obstacle‐avoidance path planning of a mobile Beacon for localisation. Issue 2 (1st June 2013)

• Record Type: Journal Article
• Authors: Wang, Yinglong; CUI, Huanqing; GUO, Qiang; SHU, Minglei
• Is Part Of: IET wireless sensor systems. Volume 3:Issue 2(2013)
• Page Start: 126
• Page End: 137
• Publication Date: 2013-06-01
• Subjects: collision avoidance -- minimax techniques -- network theory (graphs) -- wireless sensor networks -- sensor placement -- ant colony optimisation; mobile beacon -- wireless sensor network -- sensor localisation method -- beacon trajectory planning -- beacon locomotion -- obstacle avoidance path planning method -- network deployment region partitioning -- adaptive cell decomposition -- connectivity graph -- max–min ant system algorithm -- shortest global path approximation -- obstacle free cell -- sensor node deployment; collision avoidance -- minimax techniques -- network theory (graphs) -- wireless sensor networks -- sensor placement -- ant colony optimisation; mobile beacon -- wireless sensor network -- sensor localisation method -- beacon trajectory planning -- beacon locomotion -- obstacle avoidance path planning method -- network deployment region partitioning -- adaptive cell decomposition -- connectivity graph -- maxâ€"min ant system algorithm -- shortest global path approximation -- obstacle free cell -- sensor node deployment; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2011.0128
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : Location is important for most wireless sensor network applications. A potential localisation method uses a mobile beacon instead of numerous static beacons. This method includes planning a beacon trajectory as a fundamental and interesting problem. Obstacles in practical scenarios often restrict the free locomotion of beacons. This study examines the obstacle‐avoidance path‐planning method, which consists of three stages. First, the network deployment region is partitioned into cells by adaptive cell decomposition, and the corresponding connectivity graph is constructed. Second, a modified max–min ant system algorithm is provided to obtain the approximate shortest global path that covers the connectivity graph. Finally, a sequence of concentric circles of adjustable radii is utilised as the local path within an obstacle‐free cell. We prove that all sensor nodes deployed in obstacle‐free cells can be localised by using the proposed path. Simulation results show that more than 90% of the sensor nodes in the obstacle‐free region can be localised by using the given trajectory.