Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach
This work addresses energy efficiency and data freshness for UAV-based IoT networks, but it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackled the problem of optimizing energy-efficient navigation for multiple UAVs serving as mobile base stations to improve data freshness for IoT devices, achieving 3.6% and 3.13% higher energy efficiency compared to greedy and baseline DQN approaches.
In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6% and 3.13% more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.