Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
This work addresses resource management inefficiencies in IoT networks for applications requiring fresh data, though it appears incremental as it builds on existing UAV and learning methods.
The paper tackles the problem of high age of information (AoI) and energy consumption in IoT networks by proposing a learning-based framework that uses traffic prediction and deep reinforcement learning to optimize UAV trajectories and scheduling, resulting in improved AoI, scheduling accuracy, and transmission power compared to a random-walk baseline.
The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which causes high AoI, high energy consumption, and low reliability. Unmanned aerial vehicles (UAVs) as flying BSs have many advantages in minimizing the AoI, energy-saving, and throughput improvement. In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events. The learning proceeds to optimize the trajectory of multiple UAVs and their scheduling policy. First, the BS predicts the future traffic of the devices. We compare two traffic predictors: the forward algorithm (FA) and the long short-term memory (LSTM). Afterward, we propose a deep reinforcement learning (DRL) approach to optimize the optimal policy of each UAV. Finally, we manipulate the optimum reward function for the proposed DRL approach. Simulation results show that the proposed algorithm outperforms the random-walk (RW) baseline model regarding the AoI, scheduling accuracy, and transmission power.