Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155063
Title: Anchor-free multi-level self-localization in ad-hoc networks
Authors: Song, Yang
Bajaj, Ian
Rabiee, Ramtin
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2021
Source: Song, Y., Bajaj, I., Rabiee, R. & Tay, W. P. (2021). Anchor-free multi-level self-localization in ad-hoc networks. 2021 IEEE Wireless Communications and Networking Conference (WCNC). https://dx.doi.org/10.1109/WCNC49053.2021.9417602
Project: A19D6a0053 
Abstract: In this paper, we propose a multi-level localization algorithm that breaks a centralized localization problem into a cluster-level distributed localization problem, where each cluster is a centralized unit. In contrast to fully distributed localization, the cluster-level distributed scheme results in reduction in contention, communication overheads, convergence time and energy consumption because cluster heads are responsible for the intracluster positioning on behalf of the whole cluster. To generate a global map, the cluster heads communicate with their direct neighbors to carry out inter-cluster ranging and positioning. The proposed method is suitable for large ad-hoc networks where most agents are low-cost, low-power RF transceivers used for ranging only while some agents are integrated with microcomputers such as Raspberry Pis capable of running intra and inter-cluster localization algorithms. The proposed system can work without anchor nodes and thus it can be deployed in the environments such as urban canyon, inside multi-story buildings, airports, and underground shopping malls where access to anchors or Global Navigation Satellite System (GNSS) is limited or prohibitive. We exploit a hybrid of two well-known methods: multidimensional scaling (MDS) and extended Kalman filtering (EKF) to effectively construct local and global position maps, even in the absence of GNSS information, anchors, or a complete ranging matrix.
URI: https://hdl.handle.net/10356/155063
ISBN: 978-1-7281-9505-6
ISSN: 1558-2612
DOI: 10.1109/WCNC49053.2021.9417602
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/WCNC49053.2021.9417602.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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