Clustering in vehicular ad hoc network: Algorithms and challenges
•A comprehensive classification of VANET clustering algorithms: strength and weakness.•Intelligence-based VANET clustering including machine learning and fuzzy logic.•The distinction between vehicle mobility as well as network mobility strategies.•A detail study of VANET multi-hop clustering strateg...
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| Vydáno v: | Computers & electrical engineering Ročník 88; s. 106851 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.12.2020
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| ISSN: | 0045-7906, 1879-0755 |
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| Abstract | •A comprehensive classification of VANET clustering algorithms: strength and weakness.•Intelligence-based VANET clustering including machine learning and fuzzy logic.•The distinction between vehicle mobility as well as network mobility strategies.•A detail study of VANET multi-hop clustering strategies.
Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms.
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| AbstractList | •A comprehensive classification of VANET clustering algorithms: strength and weakness.•Intelligence-based VANET clustering including machine learning and fuzzy logic.•The distinction between vehicle mobility as well as network mobility strategies.•A detail study of VANET multi-hop clustering strategies.
Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms.
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| ArticleNumber | 106851 |
| Author | Mukhtaruzzaman, Mohammad Atiquzzaman, Mohammed |
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| Cites_doi | 10.1109/TITS.2016.2546555 10.1016/j.vehcom.2016.12.003 10.1155/2019/2917238 10.1109/TVT.2013.2258361 10.1109/ACCESS.2018.2886420 10.1109/TVT.2014.2323693 10.1109/TITS.2013.2265311 10.1016/j.vehcom.2017.12.003 10.1016/j.compeleceng.2020.106851 10.1109/ACCESS.2018.2837870 10.1007/s11227-014-1370-z 10.1016/j.adhoc.2012.02.006 10.1186/s13638-015-0327-0 10.1109/TVT.2015.2421277 10.1109/TVT.2011.2172473 10.1007/s11277-019-06377-z 10.1002/dac.2525 10.1109/TVT.2016.2643665 |
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