{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:12:49Z","timestamp":1780593169369,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korean government (Ministry of Science and ICT (MSIT))","doi-asserted-by":"publisher","award":["2022R1A2C1010121"],"award-info":[{"award-number":["2022R1A2C1010121"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chungbuk National University BK21 program (2023)","award":["2022R1A2C1010121"],"award-info":[{"award-number":["2022R1A2C1010121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicular clouds represent an appealing approach, leveraging vehicles\u2019 resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.<\/jats:p>","DOI":"10.3390\/s24072175","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:22:46Z","timestamp":1711628566000},"page":"2175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9410-7244","authenticated-orcid":false,"given":"Hyunseok","family":"Choi","sequence":"first","affiliation":[{"name":"Research Institute for Computer and Information Communication, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3429-5380","authenticated-orcid":false,"given":"Yoonhyeong","family":"Lee","sequence":"additional","affiliation":[{"name":"EW ELINT Technology R&D, LIG Nex1, Seongnam 13486, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8347-1278","authenticated-orcid":false,"given":"Gayeong","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Information Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0422-0647","authenticated-orcid":false,"given":"Euisin","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Information Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3971-0715","authenticated-orcid":false,"given":"Youngju","family":"Nam","sequence":"additional","affiliation":[{"name":"Research Institute for Computer and Information Communication, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MNET.2018.1700105","article-title":"Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution","volume":"32","author":"Yuan","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8638","DOI":"10.1109\/TITS.2023.3250320","article-title":"VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning","volume":"24","author":"Aung","year":"2023","journal-title":"IEEE Trans. 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