CARSO: Clustering Algorithm for Road Surveillance and Overtaking

Research and development on video streaming over vehicular ad hoc networks (VANETs) have expanded rapidly in the last few years. High-quality video streaming in the vehicular environment is very challenging due to the high nodes mobility, frequently changed network topology as well as video streaming is a high-bandwidth-demanded service and causes network congestion when different vehicles are streaming it in the vehicular network at the same time. Clustering algorithms are effective techniques to reduce network congestion by organizing the work of the network nodes. This paper proposes a clustering algorithm for road surveillance and overtaking (CARSO) which takes in the consideration the vision area, direction, and distance parameters to increase the scalability and provide the video streaming service to the highest number of vehicles. We compared our proposed algorithm with another clustering algorithm in the term of scalability and stability to prove the effectiveness of CARSO.