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Because, data mining methods have important effect to enhancement and navigate IoT-based connectivity management systems, this paper presents a technical analysis on Artificial Intelligence (AI) approaches for connectivity management systems in IoT environments. This paper provides a comprehensive perspective on vehicular communication systems, Internet of Vehicles (IoV) methods and Vehicular Ad Hoc Network (VANET) environments that have evaluated using machine learning, fuzzy logic and intelligent algorithms. Also, applied evaluation metrics to predict and detect efficient connectivity methods, succeed learning models and enhancement of IoT-based connectivity management systems are discussed and analyzed for existing AI approaches. Finally, new research directions and emerging challenges are outlined to improve the performance of advanced IoT-based connectivity management systems.<\/jats:p>","DOI":"10.3233\/jhs-220692","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T11:39:54Z","timestamp":1658489994000},"page":"221-230","source":"Crossref","is-referenced-by-count":6,"title":["Artificial intelligence mechanisms for management of QoS-aware connectivity in Internet of vehicles"],"prefix":"10.1177","volume":"28","author":[{"given":"Alireza","family":"Souri","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Hali\u00e7 University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JHS-220692_ref1","doi-asserted-by":"crossref","unstructured":"F.\u00a0Abbasi, M.\u00a0Zarei and A.M.\u00a0Rahmani, FWDP: A fuzzy logic-based vehicle weighting model for data prioritization in vehicular ad hoc 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