RATE: Reliability-Aware Task Service in Fog-Enabled IoV Environments

In this work, we introduce a reliability-aware task service framework, named RATE, designed for fog-enabled Internet of Vehicle (IoV) environments. In RATE, each fog entity makes an efficient decision for task service while considering the task requirements and availability of the resources. Existing works focus mainly on delay and energy as the main factor of decision-making. However, in a mobile IoV environment, it is vital to consider the reliability of entities serving the task. The factor of reliability affects the quality of service (QoS) offered by the task service while having an impact on the resource utilization of the nodes. To offer a reliable service while fulfilling the task demands on the fly, we propose a Q-learning-based algorithm for selecting a candidate for handling the offloaded task. On the contrary, it is important to consider the aspect of the entity offering the offloaded service. Thus, RATE uses a simulated-annealing-based algorithm for the entities to make the decision of accepting or rejecting the task based on its own resource and profit parameters. Numerical results depict that RATE reduces the delay of task service by 38%, 26%, and 18% compared to the benchmark schemes while achieving high QoS.