{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:05:30Z","timestamp":1768266330447,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T00:00:00Z","timestamp":1653782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004686","name":"Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University","doi-asserted-by":"publisher","award":["AN000533"],"award-info":[{"award-number":["AN000533"]}],"id":[{"id":"10.13039\/501100004686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. An automated car that acts as the data-source node of an intelligent Internet of vehicles or IoV system is one of these examples. Our motivation is to obtain more accurate and rapid object detection using the intelligent cameras of a smart car. The competent supervision camera of the smart automobile model utilizes multimedia data for real-time automation in real-time threat detection. The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. These actions confront real-time delays during data offloading to the cloud and synchronizing with the other nodes. The proposed model follows a cooperative machine learning technique, distributes the computational load by slicing real-time object data among analogous intelligent Internet of Things nodes, and parallel vision processing between connective edge clusters. As a result, the system increases the computational rate and improves accuracy through responsible resource utilization and active\u2013passive learning. We achieved low latency and higher accuracy for object identification through real-time multimedia data objectification.<\/jats:p>","DOI":"10.3390\/s22114133","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved Multimedia Object Processing for the Internet of Vehicles"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3097-6568","authenticated-orcid":false,"given":"Surbhi","family":"Bhatia","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}]},{"given":"Razan Ibrahim","family":"Alsuwailam","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}]},{"given":"Deepsubhra Guha","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012, India"}]},{"given":"Arwa","family":"Mashat","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,29]]},"reference":[{"key":"ref_1","unstructured":"Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., and Hui, P. 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