Analysis of Congestion Control Mechanisms for Cooperative Awareness in IoV Environments

Abstract

Traditional vehicle ad hoc networks (VANETs) have evolved toward the Internet of Vehicles (IoV) during the past ten years with the introduction of 5G communication technology and the growing number of vehicles linked to the Internet. The coexistence of IEEE 802.11p and 5G becomes critical to build a heterogeneous IoV system that benefits from both technologies, being that the IEEE 802.11p standard remains the best option for direct communications and safety-critical applications. The IEEE 1609 standard family and the ETSI ITS-G5 standard family both use the IEEE 802.11p standard as a MAC mechanism. To avoid dangerous situations, vehicles require the periodic exchange of awareness messages. With the increase in vehicle density, the MAC layer will suffer from radio channel congestion problems, which in turn will have a negative impact on the safety application requirements. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate channel congestion; this was achieved by adapting transmission parameters such as transmit power and data rate. However, several studies have demonstrated that DCC has drawbacks and suffers from poor performance when the channel load is very high. This paper investigates a new promising DCC technique called transmission timing control (TTC), to reduce the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. Obtained results showed a convergence between the applied experiments and our mathematical model, achieving an error margin of only 5%, which confirms the validity of the equation proposed.

Novel Solution Proposal for Internet of Things (IoT) Security-Based Cooperative Radio Resources System

“…The current research evaluated the Radio Resources Allocation (RRA) scheme, discusses the performance of the obtained results (Ouladdjedid et al, 2023), and also compares with other solutions in the literature based on two con gurations: rstly, with an interfered system and no optimization (with femtocells) and secondary based on an interfered system with joint MUE teams and femtocells deployment, resulting in an empirical cumulative distribution Function (ECDF) statistics of these results (Wu et al, 2023). Figure . 5 determines the ECDF for all users' throughput for performance analysis when considering macro-cells and femtocells.…”