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UAVs and Mobile Sensors Trajectories Optimization with Deep Learning Trained by Genetic Algorithm Towards Data Collection Scenario

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Abstract

In challenging environments like deserts and forests, without communication facilities, data transmission is a key challenge. To solve this problem, data collection using Unmanned Aerial Vehicles (UAVs) is widely applied in such scenarios. However, when high-density data collection is required, traditional methods using UAVs are fatally flawed due to the limited energy of UAVs and the dense distribution of collection points. Aiming to this, a data collection strategy employing mobile acquisition devices (mobile sensors), fixed sensors, and UAVs cooperation is chosen. In this strategy, mobile sensors collect data and transmit it to fixed sensors. Then, UAVs collect and carry the returned data to the data center for further processing. Correspondingly, considering data collection using mobile devices, path planning for the mobile sensors and UAVs largely determines the system’s performance. In this kind of data acquisition scheme, the previous works lack efficiency, because the genetic algorithm (GA) consumes a lot of time to get the local optimal solution and deep reinforcement learning (DRL) highly depends on the selection of reward given by experience value. Hence, deep learning trained by genetic algorithm (DL-GA) is proposed as path planning method for mobile sensors and UAVs in this paper. Simulation results show that, in data collection strategy for high-density data collection, the proposed path planning algorithm can achieve higher efficiency compared to the other algorithms.

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Acknowledgements

This work was supported in part by the Science and Technology on Communication Networks Laboratory Foundation Project under Grant 6142104200211, and in part by the Network and Data Security Key Laboratory of Sichuan Province, UESTC, under Grant NDS2021-7.

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Authors and Affiliations

  1. Information and Communication Engineering University of Electronic Science and Technology of China, Chengdu, China

    Yuwen Pan & Yuanwang Yang

  2. Science and Technology on Communication Networks Laboratory, Los Alamos, NM, USA

    Yuanwang Yang

  3. Educational Informationlization and Big Data Center, Education Department of Sichuan Province, Chengdu, China

    Hantao Liu & Wenzao Li

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  1. Yuwen Pan
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  2. Yuanwang Yang
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  3. Hantao Liu
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  4. Wenzao Li
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Contributions

Yuwen pan and Yuanwang Yang designed research, performed research and analyzed data. Yuwen Pan, Yuanwang Yang, Hantao Liu and Wenzao Li wrote and revised paper

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Correspondence to Yuanwang Yang.

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Pan, Y., Yang, Y., Liu, H. et al. UAVs and Mobile Sensors Trajectories Optimization with Deep Learning Trained by Genetic Algorithm Towards Data Collection Scenario. Mobile Netw Appl 28, 808–823 (2023). https://doi.org/10.1007/s11036-023-02106-w

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