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Task Allocation and Resource Scheduling in Vehicular Fog Computing

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Pastor, Giancarlo, Aalto University, Department of Communications and Networking, Finland
dc.contributor.author Zhu, Chao
dc.date.accessioned 2020-12-04T10:00:15Z
dc.date.available 2020-12-04T10:00:15Z
dc.date.issued 2020
dc.identifier.isbn 978-952-64-0198-0 (electronic)
dc.identifier.isbn 978-952-64-0197-3 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/87766
dc.description.abstract Future vehicles will become smarter and more connected. With the merging and sharing of data generated by onboard sensors, next-generation vehicular applications (e.g., cooperative lane change) are emerging that create a significant demand for sufficient computing resources to conduct time-critical and data-intensive tasks. Owing to the space, weight, and cost constraints, the computing capacity of most vehicles may not be high enough to handle such tasks. On the other hand, offloading these tasks to the cloud is not applicable, due to the considerable transmission delay. A new computing paradigm known as vehicular fog computing (VFC) has been proposed that pushes computing and communication resources to the edge of the network. Its key idea is to offload computational tasks from the client vehicles to fog nodes located for example at cellular base stations or buses with extra computing power. In VFC, only one-hop communication is required, which greatly shortens the transmission delay. However, due to the mobility of vehicles, the density of client vehicles and therefore the amount of tasks generated by them vary spatiotemporally. Meanwhile, the availability of fog nodes carried by vehicles, called vehicular fog nodes, depends on the driving routes of the carriers. The spatiotemporal variation in both the supply and demand of computing services adds a layer of complexity to enable reliable VFC-based services. In this dissertation, the focus is on designing task allocation and resource scheduling algorithms via various mathematical models to enable high-quality and low-latency VFC-based services for vehicular applications. The feasibility and challenges of applying VFC for real-time analytics of a crowdsourced dash camera video is investigated. Furthermore, a framework for latency and quality optimized task allocation in VFC is presented and a task-offloading framework for visual-based assisted driving is proposed. Finally, the design of a QoI and latency aware task allocation scheme for vehicle-based visual crowdsourcing is presented, which takes into account vehicle mobility and the spatiotemporal variation in the workload of vehicular fog nodes. Analytical studies were conducted in order to answer the research questions and evaluate the effectiveness of the designed algorithms, using real-world application profiles and traffic data as input. en
dc.format.extent 65 + app. 59
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 216/2020
dc.relation.haspart [Publication 1]: Chao Zhu, Giancarlo Pastor, Yu Xiao, and Antti Ylä-Jääski. Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges. IEEE Communications Magazine, vol.56, no.10, pages 58-63, Oct. 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201811095657. DOI: 10.1109/MCOM.2018.1800116
dc.relation.haspart [Publication 2]: Chao Zhu, Jin Tao, Giancarlo Pastor, Yu Xiao, Yusheng Ji, Quan Zhou, Yong Li, and Antti Ylä-Jääski. Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing. IEEE Internet of Things Journal, vol.6, no.3, pages 4150-4161, Oct. 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201810245529. DOI: 10.1109/JIOT.2018.2875520
dc.relation.haspart [Publication 3]: Chao Zhu, Yi-Han Chiang, Abbas Mehrabi, Yu Xiao, Antti Ylä-Jääski, and Yusheng Ji. Chameleon: Latency and Resolution Aware Task Offloading for Visual-based Assisted Driving. IEEE Transactions on Vehicular Technology, vol.68, no.9, pages 9038-9048, Jul. 2019. . Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201911076164. DOI: 10.1109/TVT.2019.2924911
dc.relation.haspart [Publication 4]: Chao Zhu, Yi-Han Chiang, Yu Xiao, and Yusheng Ji. FlexSensing: A QoI and Latency Aware Task Allocation Scheme for Vehicle-based Visual Crowdsourcing via Deep Q-Network. IEEE Internet of Things Journal, Accepted for Publication, Nov. 2020
dc.subject.other Computer science en
dc.title Task Allocation and Resource Scheduling in Vehicular Fog Computing en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Tietotekniikan laitos fi
dc.contributor.department Department of Computer Science en
dc.subject.keyword vehicular networks en
dc.subject.keyword edge/fog computing en
dc.subject.keyword task allocation en
dc.subject.keyword crowdsourcing en
dc.identifier.urn URN:ISBN:978-952-64-0198-0
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Ylä-Jääski, Antti, prof., Aalto University, Department of Computer Science, Finland; Xiao, Yu, asst prof., Aalto University, Department of Communications and Networking, Finland
dc.opn Bennis, Mehdi, ass., prof., University of Oulu, Finland
dc.rev Bennis, Mehdi, ass. prof. University of Oulu, Finland
dc.rev Pop, Paul, prof., Technical University of Denmark, Denmark
dc.date.defence 2020-12-23
local.aalto.acrisexportstatus checked 2020-12-29_1347
local.aalto.formfolder 2020_12_04_klo_10_26
local.aalto.archive yes


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