Predictive Path Planning of Multiple UAVs for Effective Network Hotspot Coverage

In event or disaster scenarios where network communication is jammed, it is important to provide stable network service to users within a reasonable amount of time. We propose a path planning algorithm for unmanned aerial vehicles (UAVs) to serve network traffic in hotspot areas using spatio-temporal information about requests among the region of interest (RoI). The main task of a UAV is to provide communication services to users, while preparing for future hotspots. We propose a simple yet efficient trajectory design consisting of two phases: 1) targeting traffic for a single UAV, and 2) cooperative targeting for multiple UAVs. First, each UAV selects a long-term target considering future traffic and then a short-term target considering the present traffic. When UAVs encounter other UAVs, a cooperative targeting phase ensures UAVs serve traffic in different locations or with different statuses. Our trajectory design enables a UAV to construct its own path for a continuous UAV-enabled network. Simulation and real-world dataset-based experiments confirmed that our targeting scheme provides sufficient network service in a reasonable time, with an average service rate factor of up to 0.85, and an average service completion time relative to the deadline of up to 0.23. The experimental results have demonstrated that our proposed algorithm provides more stable performance compared to other existing algorithms.