Digital twin technology has emerged as a promising strategy to augment the safety level of intelligent transportation systems by predicting the driving states of neighboring vehicles. Furthermore, with the capacity to anticipate the location of neighboring vehicles, a reduction in driving state exchanges can be achieved, which in turn decreases communication network loads and enhances system performance. However, a theoretical analysis of the performance benefits of digital twin technology in vehicular networks remains a challenge. To address this issue, this paper employs network calculus theory to derive the theoretical delay upper bounds of a digital twin-enabled vehicular network. Initially, we analyze the delays of constant interval arrival applications and Poisson arrival applications under Vehicle-to-Vehicle (V2V) communication in the Cellular Vehicle-to-Everything Mode 4 (C-V2X Mode 4) protocol. Subsequently, we examine the relationship between the driving state exchange interval and location prediction error within the digital twin framework. These two theoretical models are then integrated to formulate a method for modeling delays under varying tolerance errors. The validity of these theoretical models is confirmed by numerical outcomes. Simulation results indicate that in most scenarios, digital twin technology can diminish network loads, with a typical reduction of approximately 40% in driving state messages. Meanwhile, the average communication delay can be reduced by approximately 10%.

Working as aerial base stations, mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area. Herein, a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users, while considering the mobility of on-ground devices. In this paper, to solve this issue, we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range. Then, we propose a hybrid deployment algorithm based on the improved quick artificial bee colony. The algorithm is composed of a centralized deployment algorithm and a distributed one. The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed. Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.