{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:22:58Z","timestamp":1760232178232,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Soonchunhyang University Research Fund","award":["20210447"],"award-info":[{"award-number":["20210447"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicular caching (VC) has been considered as a promising technology to provide low end-to-end service latency and reduce the load of networks. However, it is difficult for VC to provide service continuity because of its opportunistic availability according to mobility. To mitigate this problem, we introduce a CSV: Content service offloading System with VC which can opportunistically distribute the load of the content server and support the service continuity. In CSV, the content service can be migrated between fog node (FN) and VC while maintaining the ongoing service without service disruption, which can opportunistically distribute the load of the content server and support the service continuity during migration. To assess the performance of CSV, we develop an analytical model for VC offloading efficiency. Extensive simulation results demonstrate that CSV can reduce the load of the content server compared to the conventional system.<\/jats:p>","DOI":"10.3390\/s22207967","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T22:19:53Z","timestamp":1666217993000},"page":"7967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CSV: Content Service Offloading System with Vehicular Caching"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5247-0296","authenticated-orcid":false,"given":"Yeunwoong","family":"Kyung","sequence":"first","affiliation":[{"name":"Division of Information & Communication Engineering, Kongju National University, Cheonan 31080, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2447-9613","authenticated-orcid":false,"given":"Taewon","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Internet of Things, Soonchunhyang University, Asan 31538, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/JIOT.2020.3004500","article-title":"Toward edge intelligence: Multiaccess edge computing for 5G and internet of things","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TNSE.2018.2852762","article-title":"Towards workload balancing in fog computing empowered IoT","volume":"7","author":"Fan","year":"2020","journal-title":"IEEE Trans. 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