A Comparative Analysis of Deadlock Avoidance and Prevention Algorithms for Resource Provisioning in Intelligent Autonomous Transport Systems Over 6G Infrastructure

Abstract

6G is the future of intelligent connectivity artefacts with Artificial Intelligence (AI) at its backbone. The Multi-Access Edge Computing (MEC) based 6G enabled infrastructure helps in achieving the required zero latency for autonomous Intelligent Transport Systems (ITS) with features of low power consumption, lower end-to-end latency, minimal processing/transmission overheads, higher throughput and reliability. MEC are prone to a deadlock due to the limited amount of available computational resources, resulting in fatal delays in Vehicle-to-Vehicle (V2V), Vehicles to Road Side Unit (RSU) and RSU to ITS communication. The unresolved deadlock may entail higher energy consumption that can adversely affect the Quality of Service (QoS) in terms of safety and reliability with potential threats of causing fatal accidents. Therefore, it is almost imperative to resolve the deadlocks from MEC to comply with the QoS parameters of MEC based autonomous vehicles. The asserted goals can be achieved by employing an intelligent and adaptive deadlock resolution strategy. In this paper, a deadlock-aware, and collaborative edge decision algorithm has been proposed for facilitating the seamless communication of autonomous vehicles over MEC. Additionally, deadlock avoidance and prevention schemes have been evaluated using Bankers resource request avoidance algorithm, wound wait algorithm and wait-die algorithms for resource provisioning in collaborative MEC. Furthermore, the effectiveness of deadlock avoidance and prevention algorithms in real-time scenarios has been analyzed in MEC systems. The metrics used for a comparative analysis in this research include Round-trip time, Queue wait-time and CPU utilization. The proposed algorithm shows promising results when compared with prevalent techniques.

Frameworks for Developing a 6G Communication Network to Intensify the Modern Vocational Education System

“…Applying artificial intelligence and machine learning techniques in 6G enables the network to support faster broadband, provide more massive access, and be more reliable (Du et al, 2020); to realize lower latency services (Barakabitze & Walshe, 2022;Ugwuanyi et al, 2023); and to provide higher capacity, lower cost, and higher satisfied services (Barakabitze & Walshe, 2022). Imoize et al (2021) addressed the multifaceted communication capabilities of 6G from the perspectives of technologydriven challenges and the probable solutions to them; they predicted that the new technology would contribute significantly to global sustainability and bring dramatic changes in all areas of the society, including education.…”

Empowering 6G Communication Systems With Digital Twin Technology: A Comprehensive Survey

“…Furthermore, VOLUME 10, 2022 a large number of IoT devices would be connected to the network with a demand for low power and low latency communication to support desired operations [104]. The intelligent transportation system (ITS) is another vertical industry having a substantial interest in availing communication services from the 6G network to support low latency and high-reliability safety-critical services [105]. In this way, multiple businesses and vertical industries are intertwined with mobile communication technologies and are anticipating maximum support and utilization from 6G networks.…”