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arXiv · 2003.00391

Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

Mengjie Yi·Xijun Wang·Juan Liu·Yan Zhang·Bo Bai
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Abstract

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

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Mengjie Yi, Xijun Wang, Juan Liu, Yan Zhang, Bo Bai. 2020-03-01. Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks. https://arxiv.org/abs/2003.00391

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