Flag-Based Vehicular Clustering Scheme for Vehicular Ad-Hoc Networks
Source abstract
Abstract
Clustering schemes in vehicular networks organize vehicles into logical groups. They are vital for improving network performance, accessing the medium, and enabling efficient data dissemination. Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles. However, the periodic exchange of messages overwhelms the system and reduces efficiency. This paper proposes the Flag-based Vehicular Clustering (FVC) scheme. The scheme leverages a combination of Fitness Score (FS), Link Expiration Time (LET), and clustering status flags to enable efficient cluster formation in a hybrid manner. The FVC relies on the periodic broadcast of the basic safety message in the Dedicated Short-Range Communications (DSRC) standard for exchanging the vehicle’s status, FS, and joining request. Piggybacking extra information onto the existing periodic beacon reduces the overhead of exchanging additional control messages, which is the main contribution of this work. The scheme is implemented in a hybrid manner by utilizing a Road Side Unit (RSU) to implement a clustering algorithm. This work considered the FastPAM algorithm, a fast version of the Partitioning Around Medoids (PAM) clustering algorithm, to generate a list of potential cluster heads. The FVC scheme uses the LET as the clustering metric with the FastPAM algorithm. Moreover, the Lightweight FastPAM Vehicular Clustering (LFPVC) algorithm is considered by selecting the initial cluster heads based on the FS instead of the greedy FastPAM’s build stage. In the absence of the RSU, the vehicles utilize the FS with proper back-off time to self-elect the cluster head. The hybrid FVC scheme increased the cluster lifetime by 32% and reduced the control-message overhead by 63% compared to the related work. Moreover, the LFPVC algorithm achieved similar results to the FastPAM algorithm.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Vehicular Ad Hoc Networks (VANETs) · Transportation and Mobility Innovations · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. An empirical study of DSRC V2V performance in truck platooning scenarios10.1016/j.dcan.2016.10.003 ↗
2. Dedicated short-range communications (DSRC) standards in the United States10.1109/JPROC.2011.2132790 ↗
3. Bayless · Recommended Practices for DSRC Licensing and Spectrum Management: A Guide for Management, Regulation, Deployment, and Administration for a Connected Vehicle Environment · 2015
4. A review of clustering algorithms in VANETs10.1007/s12243-020-00831-x ↗
5. Clustering in vehicular ad hoc network: Algorithms and challenges10.1016/j.compeleceng.2020.106851 ↗
6. A comprehensive survey on clustering in vehicular networks: Current solutions and future challenges10.1016/j.adhoc.2021.102729 ↗
7. Schubert · Information Systems · 2020 · 101 · 10180410.1016/j.is.2021.101804 ↗
8. Mobility prediction and routing in ad hoc wireless networks10.1002/nem.386 ↗
9. Scott’s rule10.1002/wics.103 ↗
10. Design and optimization of cluster-based DSRC and C-V2X hybrid routing10.3390/app12136782 ↗
11. Study on V2V clustering algorithm in heterogeneous network based on VANET and LTE-V2X
12. A new hybrid routing protocol using a modified K-Means clustering algorithm and continuous Hopfield network for VANET10.1109/ACCESS.2021.3068074 ↗
13. A novel routing and hybrid based clustering scheme in vehicular adhoc networks10.1016/j.ijin.2021.08.002 ↗
14. Estimating the optimal cluster number for vehicular network using Scott’s formula
15. Adaptive K-Harmonic means clustering algorithm for VANETs
16. Cluster formation and cluster head selection approach for vehicle ad-hoc network (VANETs) using K-means and Floyd-Warshall technique10.14569/IJACSA.2017.081202 ↗
17. A self-adaptable angular based K-medoid clustering scheme (SAACS) for dynamic VANETs10.3390/electronics11193071 ↗
18. Modeling and analysis of new hybrid clustering technique for vehicular ad hoc network10.3390/math10244720 ↗
19. A novel K-means powered algorithm for an efficient clustering in vehicular ad-hoc networks10.11591/ijece.v13i3.pp3140-3148 ↗
20. A new heuristic clustering algorithm based on RSU for Internet of Vehicles10.1007/s13369-019-03854-2 ↗
21. A controllable RSU and vampire moth to support the cluster stability in VANET10.5121/ijcnc.2021.13305 ↗
22. An RSU-assisted cluster head selection and backup protocol
23. Clustering scheme and destination aware context based routing protocol for VANET10.1016/j.ijin.2021.09.006 ↗
24. A centralized clustering based hybrid vehicular networking architecture for safety data delivery
25. Towards a secured clustering mechanism for messages exchange in VANET
26. On the connection availability between relay nodes in a VANET
27. Modified DMAC clustering algorithm for VANETs
28. A fuzzy multi-metric QoS-balancing gateway selection algorithm in a clustered VANET to LTE advanced hybrid cellular network10.1109/TVT.2014.2323693 ↗
29. Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications10.1016/j.adhoc.2017.10.007 ↗
30. A comparative survey of VANET clustering techniques10.1109/COMST.2016.2611524 ↗
31. Agent based dynamic clustering for hybrid VANET (ADCHV)
32. Cluster-based stable BSM dissemination system for safe autonomous platooning10.32604/cmc.2022.021237 ↗
33. Analysis of traffic stream characteristics using loop detector data