Abstract
Video streaming over Vehicular Ad Hoc Networks is a promising technique (VANETs), and it has gained great importance in the last few years. The highly dynamic topology of VANETs makes high-quality video streaming very challenging. In order to provide the most useful camera views to the vehicles, clustering and cluster head selection techniques are used. Too frequent camera view changes can be annoying; therefore, we propose a new stable clustering algorithm to ensure a stable live road surveillance service without interruptions for vehicles that do not have enough vision area. In the proposed solution, we integrated Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Fuzzy Logic Control (FLC). DBSCAN is used to form the clusters, while FLC is used to find the best cluster head for the cluster. Different parameters are utilized like density parameters for DBSCAN, and relative speed, acceleration, leadership degree and vision area for fuzzy logic. Our proposed algorithm showed better results in terms of cluster lifetime and vehicle status change. Our proposed algorithm has been compared with another clustering scheme to prove the effectiveness of our proposed algorithm.
Highlights
Vehicular Ad Hoc Network (VANET) is deemed to be one of the most vital subjects which have gained noteworthy research consideration [1]
Using the largest vision area as a single parameter to determine the best cluster head is not enough to create a stable cluster especially since the video streaming service is the most affected by the changes and reclustering the other algorithms which aim to provide stable clusters depend mainly on Road Side Unit (RSU) as a key parameter which makes it difficult to apply these algorithms in highways environments lacking to V2I technologies, this paper proposes a new stable V2V clustering algorithm entitled "A New Clustering Algorithm for Live Road Surveillance on Highways Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy Logic"
It should be noted that we chose EVAC-AV introduced in the related work section as a benchmark algorithm because it is the only clustering algorithm based on vision area estimations and use V2V mode
Summary
Vehicular Ad Hoc Network (VANET) is deemed to be one of the most vital subjects which have gained noteworthy research consideration [1]. It is part of Intelligent Transportation Systems (ITS), which is intended to provide reliable communication between vehicles and fixed roadside units and among vehicles themselves by enabling them to make contact with each other directly [2]. The communication entities, Road Side Unit (RSU) and On-Board Unit (OBU) form the backbone of VANET infrastructure. OBU is a communication device fixed in the vehicle, while RSU is a fixed unit distributed on the side of the roads or near the traffic signals [4]. Two connection types can be set up by this communication equipment, Vehicle-to-Vehicle (V2V), which enables the vehicular nodes to contact each other directly and Vehicle-toInfrastructure (V2I), in which vehicles can communicate with
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