{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:47:19Z","timestamp":1772041639164,"version":"3.50.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702277, 61872219"],"award-info":[{"award-number":["61702277, 61872219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps","award":["2020DB05"],"award-info":[{"award-number":["2020DB05"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a \u201cbig edge server\u201d to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.<\/jats:p>","DOI":"10.1145\/3401979","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T06:10:03Z","timestamp":1619071803000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":88,"title":["TripRes"],"prefix":"10.1145","volume":"17","author":[{"given":"Xiaolong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijie","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-9856","authenticated-orcid":false,"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Qufu Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Swinburne University of Technology, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, Shiga University, Japan and RIKEN Center for Advanced Intelligence Project, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"653","article-title":"Traffic flow prediction for road transportation networks with limited traffic data","volume":"16","author":"Abadi Afshin","year":"2014","journal-title":"IEEE Trans. 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