A comprehensive survey on clustering in vehicular networks: Current solutions and future challenges
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Ad Hoc Networks, 2022, 124, pp. 102729
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S1570870521002183-main (1).pdf | Published version | 2.97 MB |
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Vehicular networks are on the verge of deployment, thanks to the advancements in computation and communication technologies. This breed of ad hoc networks leverages vehicles as nodes with Vehicle-to-anything (V2X) communication paradigm. Clustering is considered one of the most important techniques used to enhance network stability, reliability, and scalability. Furthermore, clustering employs bandwidth optimization by reducing the overhead and transmission delay and helps in mitigating the hidden node problem. To date, extensive research has been done to address clustering issues in vehicular networks, and several surveys have also been published in the literature. However, a holistic approach towards clustering in vehicular networks is still lacking. In this regard, we conduct a comprehensive survey on the recent advancements in the clustering schemes for vehicular networks. We take a holistic approach to classify the algorithms by focusing on, (i) the objective of clustering mechanisms (i.e., reliability, scalability, stability, routing overhead, and delay), (ii) general-purpose clustering algorithms, (iii) application-based (i.e., QoS, MAC, security, etc.) clustering, and iv) technology-based clustering (machine learning-based, nature-inspired, fuzzy logic-based and software-defined networking-based clustering). We investigate the existing clustering mechanisms keeping in mind the factors such as cluster formation, maintenance, and management. Additionally, we present a comprehensive set of parameters for selecting cluster heads and the role of enabling technologies for cluster maintenance. Finally, we identify future research trends in clustering techniques for vehicular networks and their various breeds. This survey will act as a one-stop shop for the researchers, practitioners, and system designers to select the right clustering mechanism for their applications, services, or for their research. As a result of this survey, we can see that clustering is heavily dependent on the underlying application, context, environment, and communication paradigm. Furthermore, clustering in vehicular networks can greatly benefit from enabling technologies such as artificial intelligence.
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