Edge computing-Based mobile object tracking in internet of things

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques...

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Autores principales:	Yalong Wu, Pu Tian, Yuwei Cao, Linqiang Ge, Wei Yu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Internet of things Edge computing Architecture Mobile object tracking Vector auto regression Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/3f3bab967d9047a69c1a39a9c0eb2501
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spelling	oai:doaj.org-article:3f3bab967d9047a69c1a39a9c0eb25012021-11-22T04:33:32ZEdge computing-Based mobile object tracking in internet of things2667-295210.1016/j.hcc.2021.100045https://doaj.org/article/3f3bab967d9047a69c1a39a9c0eb25012022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2667295221000350https://doaj.org/toc/2667-2952Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.Yalong WuPu TianYuwei CaoLinqiang GeWei YuElsevierarticleInternet of thingsEdge computingArchitectureMobile object trackingVector auto regressionElectronic computers. Computer scienceQA75.5-76.95ENHigh-Confidence Computing, Vol 2, Iss 1, Pp 100045- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of things Edge computing Architecture Mobile object tracking Vector auto regression Electronic computers. Computer science QA75.5-76.95
spellingShingle	Internet of things Edge computing Architecture Mobile object tracking Vector auto regression Electronic computers. Computer science QA75.5-76.95 Yalong Wu Pu Tian Yuwei Cao Linqiang Ge Wei Yu Edge computing-Based mobile object tracking in internet of things
description	Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.
format	article
author	Yalong Wu Pu Tian Yuwei Cao Linqiang Ge Wei Yu
author_facet	Yalong Wu Pu Tian Yuwei Cao Linqiang Ge Wei Yu
author_sort	Yalong Wu
title	Edge computing-Based mobile object tracking in internet of things
title_short	Edge computing-Based mobile object tracking in internet of things
title_full	Edge computing-Based mobile object tracking in internet of things
title_fullStr	Edge computing-Based mobile object tracking in internet of things
title_full_unstemmed	Edge computing-Based mobile object tracking in internet of things
title_sort	edge computing-based mobile object tracking in internet of things
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/3f3bab967d9047a69c1a39a9c0eb2501
work_keys_str_mv	AT yalongwu edgecomputingbasedmobileobjecttrackingininternetofthings AT putian edgecomputingbasedmobileobjecttrackingininternetofthings AT yuweicao edgecomputingbasedmobileobjecttrackingininternetofthings AT linqiangge edgecomputingbasedmobileobjecttrackingininternetofthings AT weiyu edgecomputingbasedmobileobjecttrackingininternetofthings
_version_	1718418155714379776

Edge computing-Based mobile object tracking in internet of things

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