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The completion of these applications necessitates significant computational resources to perform enormous latency-sensitive\/nonlatency-sensitive and computation-intensive tasks. It is hard for vehicles to satisfy the computation requirements of these applications due to the limit computational capability of the on-board computer. To solve the problem, many works have proposed some efficient task offloading schemes in computing paradigms such as mobile fog computing (MFC) for the vehicular network. In the MFC, vehicles adopt the IEEE 802.11p protocol to transmit tasks. According to the IEEE 802.11p, tasks can be divided into high priority and low priority according to the delay requirements. However, no existing task offloading work takes into account the different priorities of tasks transmitted by different access categories (ACs) of IEEE 802.11p. In this paper, we propose an efficient task offloading strategy to maximize the long-term expected system reward in terms of reducing the executing time of tasks. Specifically, we jointly consider the impact of priorities of tasks transmitted by different ACs, mobility of vehicles, and the arrival\/departure of computing tasks, and then transform the offloading problem into a semi-Markov decision process (SMDP) model. Afterwards, we adopt the relative value iterative algorithm to solve the SMDP model to find the optimal task offloading strategy. Finally, we evaluate the performance of the proposed scheme by extensive experiments. Numerical results indicate that the proposed offloading strategy performs well compared to the greedy algorithm.<\/jats:p>","DOI":"10.1155\/2020\/8816090","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T23:31:37Z","timestamp":1599694297000},"page":"1-12","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Task Offloading for 802.11p-Based Cloud-Aware Mobile Fog Computing System in Vehicular Networks"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4899-1718","authenticated-orcid":true,"given":"Qiong","family":"Wu","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China"},{"name":"Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, China"},{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"}]},{"given":"Hongmei","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"}]},{"given":"Qiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA"}]},{"given":"Wei","family":"Yin","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China"}]},{"given":"Bo","family":"Chang","sequence":"additional","affiliation":[{"name":"Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, China"}]},{"given":"Guilu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, 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