Faster distributed localization of large numbers of nodes using clustering

Florian Klingler; Shaojie Tang; Xuefeng Liu; Falko Dressler; Christoph Sommer; Jiannong Cao

doi:10.1109/LCN.2013.6761317

Faster distributed localization of large numbers of nodes using clustering

Florian Klingler, Shaojie Tang, Xuefeng Liu, Falko Dressler, Christoph Sommer, Jiannong Cao

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

Chirp Spread Spectrum (CSS) based localization techniques are becoming more attractive as they provide improved localization accuracy and robustness compared to WiFi or ZigBee based approaches. However, a remaining problem is the necessary update frequency: In existing CSS based localization systems, the positions of the objects are determined one by one via unicast with nearby anchors instead of using broadcasts. We propose a faster distributed localization scheme for CSS based systems. A portion of nodes are considered cluster heads; they determine the locations of un-localized nodes by dynamically increasing the transmission power. Our novel scheme not only fully utilizes the spatial redundancy, which is crucial for speeding up the localization process. By also allowing to establish new anchors in a two-hop range, we can further increase speed without significantly influencing localization error. The performance of the proposed method is demonstrated through simulation.

Original language	English
Title of host publication	Proceedings of the 38th Annual IEEE Conference on Local Computer Networks, LCN 2013
Publisher	IEEE Computer Society
Pages	711-714
Number of pages	4
ISBN (Print)	9781479905379
DOIs	https://doi.org/10.1109/LCN.2013.6761317
Publication status	Published - 1 Jan 2013
Event	38th Annual IEEE Conference on Local Computer Networks, LCN 2013 - Sydney, NSW, Australia Duration: 21 Oct 2013 → 24 Oct 2013

Conference

Conference	38th Annual IEEE Conference on Local Computer Networks, LCN 2013
Country/Territory	Australia
City	Sydney, NSW
Period	21/10/13 → 24/10/13

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture

More information

10.1109/LCN.2013.6761317

Cite this

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Klingler, F, Tang, S, Liu, X, Dressler, F, Sommer, C & Cao, J 2013, Faster distributed localization of large numbers of nodes using clustering. in Proceedings of the 38th Annual IEEE Conference on Local Computer Networks, LCN 2013., 6761317, IEEE Computer Society, pp. 711-714, 38th Annual IEEE Conference on Local Computer Networks, LCN 2013, Sydney, NSW, Australia, 21/10/13. https://doi.org/10.1109/LCN.2013.6761317

Faster distributed localization of large numbers of nodes using clustering. / Klingler, Florian; Tang, Shaojie; Liu, Xuefeng et al.
Proceedings of the 38th Annual IEEE Conference on Local Computer Networks, LCN 2013. IEEE Computer Society, 2013. p. 711-714 6761317.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - Faster distributed localization of large numbers of nodes using clustering

AU - Klingler, Florian

AU - Tang, Shaojie

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AU - Dressler, Falko

AU - Sommer, Christoph

AU - Cao, Jiannong

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AB - Chirp Spread Spectrum (CSS) based localization techniques are becoming more attractive as they provide improved localization accuracy and robustness compared to WiFi or ZigBee based approaches. However, a remaining problem is the necessary update frequency: In existing CSS based localization systems, the positions of the objects are determined one by one via unicast with nearby anchors instead of using broadcasts. We propose a faster distributed localization scheme for CSS based systems. A portion of nodes are considered cluster heads; they determine the locations of un-localized nodes by dynamically increasing the transmission power. Our novel scheme not only fully utilizes the spatial redundancy, which is crucial for speeding up the localization process. By also allowing to establish new anchors in a two-hop range, we can further increase speed without significantly influencing localization error. The performance of the proposed method is demonstrated through simulation.

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Faster distributed localization of large numbers of nodes using clustering

Abstract

Conference

ASJC Scopus subject areas

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