Name
Title | ||
|---|---|---|
UAV-Assisted Content Delivery in Intelligent Transportation Systems-Joint Trajectory Planning and Cache Management |
Abstract | ||
|---|---|---|
Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TITS.2020.3020220 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Unmanned aerial vehicle (UAV),UAVs’ trajectories,resource allocation,Deep Reinforcement Learning,Vehicular Ad-hoc Networks (VANETs) | Journal | 22 |
Issue | ISSN | Citations |
8 | 1524-9050 | 3 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Al-Hilo | 1 | 7 | 1.43 |
| Moataz Shoukry Samir | 2 | 58 | 6.56 |
| Chadi Assi | 3 | 1357 | 137.73 |
| sanaa sharafeddine | 4 | 145 | 23.26 |
| Dariush Ebrahimi | 5 | 126 | 12.81 |