Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot

Feng, S; Shi, H; Huang, L; Shen, S; Yu, S; Peng, H; Wu, C

Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot

Feng, S Shi, H Huang, L Shen, S Yu, S

Peng, H Wu, C

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2021, 182, pp. 103053-103053
Issue Date:: 2021-05-15

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Field	Value	Language
dc.contributor.author	Feng, S
dc.contributor.author	Shi, H
dc.contributor.author	Huang, L
dc.contributor.author	Shen, S
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Peng, H
dc.contributor.author	Wu, C
dc.date.accessioned	2021-04-28T07:43:11Z
dc.date.available	2021-04-28T07:43:11Z
dc.date.issued	2021-05-15
dc.identifier.citation	Journal of Network and Computer Applications, 2021, 182, pp. 103053-103053
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/148496
dc.description.abstract	In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2021.103053
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot
dc.type	Journal Article
utslib.citation.volume	182
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-28T07:43:09Z
pubs.publication-status	Accepted
pubs.volume	182

Abstract:

In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.

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http://hdl.handle.net/10453/148496