V2V-CoVAD: A vehicle-to-vehicle cooperative video alert dissemination mechanism for Internet of Vehicles in a highway environment

Nowadays, traffics on the roads are getting more and more congested due to the fast-rising number of vehicles and the increasing need of citizens for mobility. Accidents happen frequently and result great loss of lives and properties. Internet of Vehicles (IoVs), as an important part of Internet of Things (IoTs), is placed high hope for enhancing traffic safety and controlling the damage caused by traffic accidents. According to statistics, most accidents can be avoided if drivers can be notified of a danger timely, so the key to minimize the damage introduced by road traffic accidents is the realization of fast and efficient data dissemination. However, great challenges exist due to the transmission characteristics of IoVs, such as high-speed movement of vehicles and dynamic changing network topologies. In this paper, we propose a vehicle-to-vehicle cooperative video alert dissemination mechanism for transmitting accident video in the highway scenario of IoVs. A two-way cooperative transmission strategy is designed. Vehicles in the same direction of accident vehicle are formed into clusters and communicate within clusters, while vehicles in the opposite direction select relay vehicles to help spreading video fast and reliably. The difficulties brought in by the characteristics of IoVs are solved through careful considerations of multiple factors such as vehicles' speeds, locations, distances, channel conditions and data receiving statuses in the design of the mechanism. Moreover, Scalable Video Coding (SVC) technology is used for encoding the original accident video, to take care of the performance degradation caused by the heterogeneity of vehicles in different locations. Instantly Decodable Network Coding (IDNC) technology is adopted during the cooperative transmission to further improve the transmission efficiency and reliability. Simulation results justify the proposed mechanism that it effectively shortens the accident video transmission delay, increases the success warning ratio, enhances the reconstructed video quality and improves the user satisfaction. (C) 2021 Elsevier Inc. All rights reserved.