Optimizing Energy Efficiency in UAV-Assisted Wireless Sensor Networks With Reinforcement Learning PPO2 Algorithm

Sun, Aijing; Sun, Chi; Du, Jianbo; Wei, De

doi:10.1109/jsen.2023.3324845

IEEE Sensors J.

2023

DOI: 10.1109/jsen.2023.3324845

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Optimizing Energy Efficiency in UAV-Assisted Wireless Sensor Networks With Reinforcement Learning PPO2 Algorithm

Aijing Sun,

Chi Sun,

Jianbo Du

et al.

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2024

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Cited by 3 publications

References 42 publications

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Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential Approach

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IEEE Sensors J.

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A Low-Cost UAV Swarm Relative Positioning Architecture Based on BDS/Barometer/UWB

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Wang

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Deep Reinforcement Learning for UAV-Based SDWSN Data Collection

Karegar,

Al-Hamid,

Chong

2024

Future Internet

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Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery.

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Optimizing Energy Efficiency in UAV-Assisted Wireless Sensor Networks With Reinforcement Learning PPO2 Algorithm

Cited by 3 publications

References 42 publications

Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential Approach

Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential Approach

A Low-Cost UAV Swarm Relative Positioning Architecture Based on BDS/Barometer/UWB

Deep Reinforcement Learning for UAV-Based SDWSN Data Collection

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