Adaptive and Optimized Control Channel Utilization in Vehicular Ad Hoc Networks

Abstract

Vehicular ad hoc networks (VANETs) are counted as a state-of-art technology which can be considered to provide reliable communication which is increase the safety and convenience of drivers in the road. Due to channel congestion occurrence in both highway and urban scenario, many VANET’s safety applications face degradation in their quality of service especially in a high-density environment. To improve the performance, reliability, and safety of VANETs, message overhead on the standard wireless channel dedicated short-range communications (DSRC) should be alleviated to guarantee safety-related applications. To this end, this paper proposes an approach to optimize the utilization of the control channel (CCH) and make the control channel interval (CCHI) adaptive to the emergency cases in the vehicular scenarios. More precisely, when there is an emergency cases, a part of service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery. When a vehicle intended to transmit the safety message to the surround vehicles, the proposed method granted that CCH is allocated to meet the traffic safety demands; then, the safety messages disseminate properly to the transmission range. A part of the service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery, Then, applying the laying chicken algorithm (LCA) to solve the derived CCH optimized utilization. Finally, extensive simulations are carried out to validate the proposed optimized CCH utilization. To validate the performance of the proposed adaptive congestion control approach (ACCA), we used the network simulator Ns-3 [35] to simulate the vehicle’s environment, after generating vehicles’ mobility traces of the nodes using the network simulator for urban mobility (SUMO) with OpenStreetMap (OSM). The simulation indicates that our congestion algorithm (ACCA) performs better service quality than other existing algorithms. We got a better network connection regarding safety messages, beacon rate with time, and packet length for different vehicles’ densities.

Enhanced text classification through an improved discrete laying chicken algorithm

“…The success of the algorithm depends on how well these parameters are chosen and how effectively they are combined to solve the optimization problem at hand. It has been applied to many different optimization problems (Ghafoor et al, 2018;Hosseini et al, 2021;Mohammed et al, 2022;Yanto et al, 2020).…”

“…Laying chicken algorithm (LCA) is a recent algorithm (Hosseini, 2017) that has been successfully employed as a heuristic optimization problems in (Ghafoor et al, 2018;Hosseini et al, 2021;Mohammed et al, 2022;Yanto et al, 2020). It is a relatively new metaheuristic optimization algorithm that is inspired by the behaviour of laying chickens.…”

“…Its ease of implementation and user-friendly nature render it accessible to a wide range of users, even those without extensive expertise in optimization or computer science. One of the standout advantages of LCA lies in its robustness and efficiency in finding high-quality solutions across diverse optimization challenges (Ghafoor et al, 2018;Hosseini et al, 2021;Mohammed et al, 2022;Yanto et al, 2020). Its ability to strike a balance between exploration and exploitation during the search process is particularly advantageous, allowing it to converge swiftly to near-optimal solutions.…”

“…This rapid convergence not only saves computational resources but also makes it a compelling choice for time‐sensitive applications. Furthermore, the scalability of LCA, particularly its effectiveness in handling large‐scale optimization problems, positions it as a promising tool for real‐world applications (Hosseini et al, 2021; Mohammed et al, 2022). This scalability factor is especially relevant in text classification, where the exponential growth of digital text documents demands efficient and scalable feature selection techniques.…”