Optimizing quality-of-service (QOS) using semantic sensing and digital-twin in pro-dynamic internet of vehicles (IOV)

Abstract

The emerging autonomous driving industry expects real-time information to be communicated in less amount of time. Most of the extant research works on deterministic or stochastic channels, which are deemed unrealistic for pro-dynamic Internet of Vehicles (IoV) communications. Semantic communication provides a novel concept of serving high-mobility vehicles with faster vehicular communications by using digital twin (DT) technology. However, the low-latency demand, intermittent connectivity, and signal attenuation in the IoV canyon pose big challenges. To facilitate the efficient functioning of Intelligent Transport Systems (ITS) applications, we integrate DT, which is a co-simulation of software such as CARLA, SUMO, python, etc., to improve the semantic communication and quality of service (QoS) of the IoV scenario. Further, we have formulated a vehicular sensing and computation model that incorporates system cost and DT migration cost as their key metrics to evaluate the QoS of the system. We have proposed a pro-dynamic algorithm based on digital-twin deep reinforcement learning (DT-DRL) to decode the QoS maximization problem. Numerical results reveal the superiority of our method by decreasing the cost of the system and improving latency, maintaining the semantic real-time communication in IoV.