Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems
Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the...
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Veröffentlicht in: | IEEE internet of things journal 2020-01, Vol.7 (1), p.773-785 |
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description | Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results. |
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In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.</description><identifier>ISSN: 2327-4662</identifier><identifier>EISSN: 2327-4662</identifier><identifier>DOI: 10.1109/JIOT.2019.2953047</identifier><identifier>CODEN: IITJAU</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>80211p ; Ad hoc networks ; Autonomous vehicles ; Cloud computing ; Computation offloading ; Delay ; Delays ; Edge computing ; Fans ; fog computing ; Internet of Things ; Iterative algorithms ; Iterative methods ; Markov analysis ; Markov processes ; offloading ; semi-Markov decision process (SMDP) ; Task analysis ; Vehicles ; vehicular networks</subject><ispartof>IEEE internet of things journal, 2020-01, Vol.7 (1), p.773-785</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-15e3794478372a7b1701ff4a84a49fe851e315180e3a962c1ed6189a2c33eff23</citedby><cites>FETCH-LOGICAL-c293t-15e3794478372a7b1701ff4a84a49fe851e315180e3a962c1ed6189a2c33eff23</cites><orcidid>0000-0002-4899-1718 ; 0000-0002-3512-7103 ; 0000-0003-4940-7453 ; 0000-0001-8318-5626 ; 0000-0001-6913-6203 ; 0000-0002-0658-6079</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8896964$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8896964$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wu, Qiong</creatorcontrib><creatorcontrib>Liu, Hanxu</creatorcontrib><creatorcontrib>Wang, Ruhai</creatorcontrib><creatorcontrib>Fan, Pingyi</creatorcontrib><creatorcontrib>Fan, Qiang</creatorcontrib><creatorcontrib>Li, Zhengquan</creatorcontrib><title>Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems</title><title>IEEE internet of things journal</title><addtitle>JIoT</addtitle><description>Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.</description><subject>80211p</subject><subject>Ad hoc networks</subject><subject>Autonomous vehicles</subject><subject>Cloud computing</subject><subject>Computation offloading</subject><subject>Delay</subject><subject>Delays</subject><subject>Edge computing</subject><subject>Fans</subject><subject>fog computing</subject><subject>Internet of Things</subject><subject>Iterative algorithms</subject><subject>Iterative methods</subject><subject>Markov analysis</subject><subject>Markov processes</subject><subject>offloading</subject><subject>semi-Markov decision process (SMDP)</subject><subject>Task analysis</subject><subject>Vehicles</subject><subject>vehicular networks</subject><issn>2327-4662</issn><issn>2327-4662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkFFLwzAUhYMoOKY_QHwJ-NyZm6Rp8qjT6WSwh1VfQ-xuts6urU0r7N_bsiE-3fvwnXPgI-QG2ASAmfu3-TKdcAZmwk0smEzOyIgLnkRSKX7-778k1yHsGGN9LAajRiR9wsIdohWWIW_zH6SpC1906X1RuXVebmhe0naLVDPeb9XRowu4ph-4zbOucA2dVRs6rfZ11w7w6hBa3IcrcuFdEfD6dMfkffacTl-jxfJlPn1YRBk3oo0gRpEYKRMtEu6ST0gYeC-dlk4ajzoGFBCDZiicUTwDXCvQxvFMCPSeizG5O_bWTfXdYWjtruqasp-0XAitlBRa9hQcqaypQmjQ27rJ9645WGB28GcHf3bwZ0_--sztMZMj4h-vtVGmL_0FxUdpSg</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Wu, Qiong</creator><creator>Liu, Hanxu</creator><creator>Wang, Ruhai</creator><creator>Fan, Pingyi</creator><creator>Fan, Qiang</creator><creator>Li, Zhengquan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-4899-1718</orcidid><orcidid>https://orcid.org/0000-0002-3512-7103</orcidid><orcidid>https://orcid.org/0000-0003-4940-7453</orcidid><orcidid>https://orcid.org/0000-0001-8318-5626</orcidid><orcidid>https://orcid.org/0000-0001-6913-6203</orcidid><orcidid>https://orcid.org/0000-0002-0658-6079</orcidid></search><sort><creationdate>202001</creationdate><title>Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems</title><author>Wu, Qiong ; Liu, Hanxu ; Wang, Ruhai ; Fan, Pingyi ; Fan, Qiang ; Li, Zhengquan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-15e3794478372a7b1701ff4a84a49fe851e315180e3a962c1ed6189a2c33eff23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>80211p</topic><topic>Ad hoc networks</topic><topic>Autonomous vehicles</topic><topic>Cloud computing</topic><topic>Computation offloading</topic><topic>Delay</topic><topic>Delays</topic><topic>Edge computing</topic><topic>Fans</topic><topic>fog computing</topic><topic>Internet of Things</topic><topic>Iterative algorithms</topic><topic>Iterative methods</topic><topic>Markov analysis</topic><topic>Markov processes</topic><topic>offloading</topic><topic>semi-Markov decision process (SMDP)</topic><topic>Task analysis</topic><topic>Vehicles</topic><topic>vehicular networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu, Qiong</creatorcontrib><creatorcontrib>Liu, Hanxu</creatorcontrib><creatorcontrib>Wang, Ruhai</creatorcontrib><creatorcontrib>Fan, Pingyi</creatorcontrib><creatorcontrib>Fan, Qiang</creatorcontrib><creatorcontrib>Li, Zhengquan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE internet of things journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu, Qiong</au><au>Liu, Hanxu</au><au>Wang, Ruhai</au><au>Fan, Pingyi</au><au>Fan, Qiang</au><au>Li, Zhengquan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems</atitle><jtitle>IEEE internet of things journal</jtitle><stitle>JIoT</stitle><date>2020-01</date><risdate>2020</risdate><volume>7</volume><issue>1</issue><spage>773</spage><epage>785</epage><pages>773-785</pages><issn>2327-4662</issn><eissn>2327-4662</eissn><coden>IITJAU</coden><abstract>Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. 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subjects | 80211p Ad hoc networks Autonomous vehicles Cloud computing Computation offloading Delay Delays Edge computing Fans fog computing Internet of Things Iterative algorithms Iterative methods Markov analysis Markov processes offloading semi-Markov decision process (SMDP) Task analysis Vehicles vehicular networks |
title | Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems |
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