Age of Information Aware UAV Deployment for Intelligent Transportation Systems

• Published in: IEEE transactions on intelligent transportation systems 2022-03, Vol.23 (3), p.2705-2715
• Main Authors: Han, Rui, Wen, Yongqing, Bai, Lin, Liu, Jianwei, Choi, Jinho
• Format: Article
• Language: English
• Subjects: Accident prevention; age-of-information (AoI); Constraint modelling; Convexity; Data collection; Data processing; Data retrieval; Data transmission; deployment optimization; Freshness; Intelligent transportation; Intelligent transportation systems; Internet of Vehicles (IoV); Network latency; Optimization; Packets (communication); Quality of service; Roads; Task analysis; Traffic congestion; Transportation; Transportation networks; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); Urban areas; Wireless communication
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3117974

The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.