A Traffic Density-Based Congestion Control Method for VANETs

Abstract

This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.

A Hybrid Power-Rate Management Strategy in Distributed Congestion Control for 5G-NR-V2X Sidelink Communications

“…The comparison with the existing ETSI design shows that this design improves the perceived quality and reduces the message generation rate. In [ 17 ], the authors introduce a traffic density-based congestion control algorithm (TDCCA) that incorporates vehicle IDs into their respective CAMs and utilizes TRC-based DCC to enhance model parameter efficiency. The algorithm considers a range of network conditions, from non-saturated to saturated, as well as sparsely dispersed and congested networks.…”

A NEW Technique for Disseminating Knowledge Concerning VANET Traffic Using Crocodile Hunting Search

“…VANETs contribute to some important systems, of which a few for an increase in safety are the crash avoidance system, cooperative adaptive cruise control, and intersection management. Vehicular Ad Hoc Networks (VANETs) are designed in such a way that they will permit communication between vehicles so that one can monitor real-time traffic congestion and, in turn, optimize traffic signal operations and give dynamic route guidance as a means of traffic control [8]. Such an approach would allow for applications in the field of remote car diagnostics, intelligent parking solutions, or even entertainment services for travelers.…”

“…Some of the greatest challenges are security and authentication, which have to be met for proper affirmation of only allowed parties as participants in secure communications to avoid harmful attacks and access from secret information. Another big challenge is maintaining user privacy and allowing the exchange of relevant data of traffic [8]. Figure 1 depicts the routing process in practical vehicular ad hoc networks.…”

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“…Compared to omnidirectional antennas, this seems to be an attractive advantage while using directional antennas. In case of successful transmission scenarios while employing directional antennas, the node capacity shrivels to nil with increased number of nodes [2].…”