A Novel AI-Based Framework for AoI-Optimal Trajectory Planning in UAV-Assisted Wireless Sensor Networks

Information freshness, which is characterized by a new performance metric called age of information (AoI), significantly influences decision making in numerous applications. In wireless sensor networks, unmanned aerial vehicle (UAV) has been widely adopted for fresh data collection. The key to applying UAV lies in UAV trajectory planning. Considering several fixed waypoints in UAV trajectory, the trajectory planning is an NP-hard combinatorial optimization problem, and is difficult to solve in practice. To well balance between the accuracy and efficiency, we propose an end-to-end AI-based framework in this paper to deal with the UAV trajectory planning within two stages. First, the hover positions of UAV and data transmission time are decided using a clustering module. Then, the AoI-minimal flight path is obtained through a neural trajectory solver. Compared with classic heuristic algorithms, the proposed AI-based framework achieves a smaller AoI with two orders of magnitude lower computational time. Besides, the proposed AI-based framework can be easily generalized to larger-scale scenarios (e.g., up to 2,000 sensor nodes) which cannot be solved by exact algorithms (e.g., dynamic programming) in a limited time. Moreover, the AI-based framework is comparable in accuracy with the commercial open-source solver Google OR-tools, but the efficiency is increased by 200%.

Automated summary

This paper proposes an AI-based framework for UAV trajectory planning, consisting of two stages: determining hover positions and transmission time using a clustering module, and obtaining the minimum AoI flight path through neural trajectory solver. The proposed framework achieves significantly lower computational time and comparable accuracy to classic heuristic algorithms and commercial open-source solver.