{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:07:20Z","timestamp":1777705640677,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:p>In Vehicular cloud computing (VCC), at the time of congestion, to deal with traffic, the vehicles\u2019 underutilized resources are shared; subsequently, these resources aren\u2019t constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC\u2019s resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system\u2019s performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform.<\/jats:p>","DOI":"10.3233\/jifs-223522","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T12:48:18Z","timestamp":1674564498000},"page":"6483-6495","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid Square Shaped Adaptive Neuro-Fuzzy Interference with M-PeSOA model for optimal path selection"],"prefix":"10.1177","volume":"44","author":[{"given":"R.","family":"Ruhin Kouser","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Engineering, Presidency University, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Manikandan","sequence":"additional","affiliation":[{"name":"Department of ECE, Rajalakshmi Engineering College, Chennai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-223522_ref1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1007\/s12083-018-0677-6","article-title":"An efficient model for vehicular cloud computing with prioritizing computing resources","volume":"12","author":"Tahmasebi","year":"2019","journal-title":"Peer-to-Peer Networking and Applications"},{"issue":"2","key":"10.3233\/JIFS-223522_ref2","first-page":"2647","article-title":"A resource management algorithm for virtual machine migration in vehicular cloud computing","volume":"67","author":"Pande","year":"2021","journal-title":"Continua"},{"key":"10.3233\/JIFS-223522_ref3","doi-asserted-by":"crossref","first-page":"137052","DOI":"10.1109\/ACCESS.2020.3011705","article-title":"Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks","volume":"8","author":"Khayyat","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-223522_ref4","doi-asserted-by":"crossref","first-page":"100213","DOI":"10.1016\/j.vehcom.2019.100213","article-title":"RSEAP: RFID based secure and efficient authentication protocol for vehicular cloud computing","volume":"22","author":"Kumar","year":"2020","journal-title":"Vehicular Communications"},{"issue":"3","key":"10.3233\/JIFS-223522_ref5","doi-asserted-by":"crossref","DOI":"10.1177\/1550147719837859","article-title":"Joint communication and computing resource allocation in vehicular edge computing","volume":"15","author":"Sun","year":"2019","journal-title":"International Journal of Distributed Sensor Networks"},{"issue":"3","key":"10.3233\/JIFS-223522_ref6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MNET.2018.1700347","article-title":"Integrated authentication and key agreement framework for vehicular cloud computing","volume":"32","author":"Jiang","year":"2018","journal-title":"IEEE Network"},{"key":"10.3233\/JIFS-223522_ref7","doi-asserted-by":"crossref","first-page":"100311","DOI":"10.1016\/j.vehcom.2020.100311","article-title":"RSEAP2: An enhanced version of RSEAP, an RFID based authentication protocol for vehicular cloud computing","volume":"28","author":"Safkhani","year":"2021","journal-title":"Vehicular Communications"},{"issue":"5","key":"10.3233\/JIFS-223522_ref8","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1109\/JIOT.2017.2690961","article-title":"Resource allocation in vehicular cloud computing systems with heterogeneous vehicles and roadside units","volume":"5","author":"Lin","year":"2017","journal-title":"IEEE Internet of Things Journal"},{"key":"10.3233\/JIFS-223522_ref9","doi-asserted-by":"crossref","unstructured":"Sheikh M.S. , Liang J. , Wang W. 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