AoI-driven Fresh Situation Awareness by UAV Swarm: Collaborative DRL-based Energy-Efficient Trajectory Control and Data Processing

Abstract

In many delay-sensitive monitoring and surveillance applications, unmanned aerial vehicles (UAVs) can act as edge servers in the air to coordinate with base stations (BSs) for in-situ data collection and processing to achieve real-time situation awareness. In order to ensure the long-term freshness requirements of situation awareness, a swarm of UAVs need to fly frequently among different sensing regions. However, nonstop flying and data processing may quickly drain the batteries armed in UAVs, hence an energy-efficient algorithm for UAVs' dynamic trajectory planning as well as proper data offloading decision is highly desirable. To better model the problem, we propose a freshness function based on the concept of Age-of-Information to express the freshness of situation awareness. We adopt a novel multi-agent deep reinforcement learning (DRL) algorithm with global-local rewards to solve the established continuous online decision-making problem involving many UAVs for achieving efficient collaborative control. Extensive simulation results show that our proposed algorithm can achieve the most efficient performance compared to six other baselines, i.e., our algorithm is able to significantly reduce the energy consumption while keeping the global situation awareness at a very fresh level under the fast changing environmental dynamics.