A traffic‐centric fuzzy approach for solving the starvation problem of cooperative awareness messages in vehicular ad hoc networks

Abstract

Congestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter-vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic-centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter-vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi-direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even-driven messages. The obtained simulation results through OMNET++ in both single and multi-direction scenarios show the efficiency of FDCC in terms of the end-to-end delay, network throughput, and packet delivery ratio for event-driven messages as well as essential CAMs.

Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

“…IoV's primary objective is to increase driver and passenger comfort and safety. 23,24 Numerous efforts have been made to reduce accidents and enhance traffic flow. Each vehicle in the IoV is outfitted with technology that enables communication between the drivers and the road infrastructure.…”

“…IoV is a massive interactive network that allows for communication between nearby vehicles, moving vehicles, and stationary infrastructures often mounted on the side of the road. IoV's primary objective is to increase driver and passenger comfort and safety 23,24 …”

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

“…Abdollah et al 7 proposed an approach to addressing this issue to some extent. In this paper, the starvation problem was dramatically reduced using removing unnecessary event‐driven messages for processing CAMs.…”

“…The Internet of Things (IoT) technology has faced an increase in the number of dynamic objects, including connected vehicles, which formed the Internet of Vehicles (IoV), and made our lives easier, safer, and smarter 3–5 . VANETs, as the central infrastructure of the IoV ecosystem, aim to help drivers with possible inter‐vehicle communication (IVC) and real‐time data propagation even in high mobility, helping safety driving and receiving alerts for road hazardous 6,7 . Plug‐in electric vehicles (PEVs) and unmanned aerial vehicles (UAVs), as an emerging part of energy‐aware VANETs, have a significant impact on the formation of the IoV 8 .…”

“…[3][4][5] VANETs, as the central infrastructure of the IoV ecosystem, aim to help drivers with possible inter-vehicle communication (IVC) and realtime data propagation even in high mobility, helping safety driving and receiving alerts for road hazardous. 6,7 Plug-in electric vehicles (PEVs) and unmanned aerial vehicles (UAVs), as an emerging part of energy-aware VANETs, have a significant impact on the formation of the IoV. 8 Connected vehicles in IoV are intelligent vehicles with a complicated software platform.…”