CGA-based deadlock solving strategies towards vehicle sensing systems

Abstract

Vehicle sensing system is an important research topic in the research field of Internet-of-Vehicles (IoV). Reliability and real-time performance of vehicle sensing systems are greatly influenced when deadlock happens. When a deadlock is detected, identifying the optimal deadlock solving strategy can ensure that the system goes back to normal state quickly. In order to address this issue, this paper proposes an efficient deadlock solving method. Firstly, the deadlock problem in a vehicle sensing system is analyzed based on four deadlock occurring conditions (i.e., mutual exclusion, hold and wait, no preemption, and circular wait). Secondly, an optimization model is built to combine the quantity and cost of tasks in vehicle sensing systems. After that, a co-evolutionary genetic algorithm (CGA) is developed to search the optimal deadlock solving strategy. Finally, experiments by simulation are conducted and the experimental results show the efficiency of the proposed deadlock solving method for vehicle sensing systems.