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- Publisher Website: 10.1109/JIOT.2020.3023000
- Scopus: eid_2-s2.0-85100812632
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Article: Edge Learning With Unmanned Ground Vehicle: Joint Path, Energy, and Sample Size Planning
Title | Edge Learning With Unmanned Ground Vehicle: Joint Path, Energy, and Sample Size Planning |
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Authors | |
Keywords | Planning Wireless communication Internet of Things Path planning Task analysis |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER288-ELE |
Citation | IEEE Internet of Things Journal, 2021, v. 8 n. 4, p. 2959-2975 How to Cite? |
Abstract | Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Things (IoT). However, due to the limited transmit power at IoT devices, collecting the sensing data in EL systems is a challenging task. To address this challenge, this article proposes to integrate unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs? This article further proposes a graph-based path planning model, a network energy consumption model, and a sample size planning model that characterizes F-measure as a function of the minority class sample size. With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. To this end, it is proved that each IoT device should be served only once along the path, thus the problem dimension is significantly reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)-based algorithm is derived, which converges in expectation to the optimal solution to the JPESP problem. Simulation results under different task scenarios show that our optimization schemes outperform the fixed EL and the full path EL schemes. |
Persistent Identifier | http://hdl.handle.net/10722/296365 |
ISSN | 2023 Impact Factor: 8.2 2023 SCImago Journal Rankings: 3.382 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, D | - |
dc.contributor.author | Wang, S | - |
dc.contributor.author | Wen, Z | - |
dc.contributor.author | Cheng, L | - |
dc.contributor.author | Wen, M | - |
dc.contributor.author | Wu, YC | - |
dc.date.accessioned | 2021-02-22T04:54:16Z | - |
dc.date.available | 2021-02-22T04:54:16Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Internet of Things Journal, 2021, v. 8 n. 4, p. 2959-2975 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296365 | - |
dc.description.abstract | Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Things (IoT). However, due to the limited transmit power at IoT devices, collecting the sensing data in EL systems is a challenging task. To address this challenge, this article proposes to integrate unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs? This article further proposes a graph-based path planning model, a network energy consumption model, and a sample size planning model that characterizes F-measure as a function of the minority class sample size. With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. To this end, it is proved that each IoT device should be served only once along the path, thus the problem dimension is significantly reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)-based algorithm is derived, which converges in expectation to the optimal solution to the JPESP problem. Simulation results under different task scenarios show that our optimization schemes outperform the fixed EL and the full path EL schemes. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER288-ELE | - |
dc.relation.ispartof | IEEE Internet of Things Journal | - |
dc.rights | IEEE Internet of Things Journal. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx 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. | - |
dc.subject | Planning | - |
dc.subject | Wireless communication | - |
dc.subject | Internet of Things | - |
dc.subject | Path planning | - |
dc.subject | Task analysis | - |
dc.title | Edge Learning With Unmanned Ground Vehicle: Joint Path, Energy, and Sample Size Planning | - |
dc.type | Article | - |
dc.identifier.email | Wen, M: mwwen@hku.hk | - |
dc.identifier.email | Wu, YC: ycwu@eee.hku.hk | - |
dc.identifier.authority | Wu, YC=rp00195 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JIOT.2020.3023000 | - |
dc.identifier.scopus | eid_2-s2.0-85100812632 | - |
dc.identifier.hkuros | 321340 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 2959 | - |
dc.identifier.epage | 2975 | - |
dc.identifier.isi | WOS:000616317000066 | - |
dc.publisher.place | United States | - |