Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink
  in Markovian IoT Models
Eslam Eldeeb Mohammad Shehab Hirley Alves
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.