Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Operating unmanned aerial vehicles (UAVs) for data collection is a promising approach across various practical domains, offering flexibility in challenging environments to facilitate data collection within sensor networks (SNs). However, UAV-assisted data collection in SNs faces several challenges,...
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| Format: | masterThesis |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/10725/17069 https://doi.org/10.26756/th.2023.812 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
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| Summary: | Operating unmanned aerial vehicles (UAVs) for data collection is a promising approach across various practical domains, offering flexibility in challenging environments to facilitate data collection within sensor networks (SNs). However, UAV-assisted data collection in SNs faces several challenges, primarily due to energy constraints at both UAV and SN nodes and the inefficiencies caused by collecting redundant data. Addressing these issues is crucial for improving the efficiency of UAV-assisted data collection. Considering the value of information (VoI) urges the collection of the newly generated data and gives less importance for collecting old data values. Moreover, the collection of all data may lead to collect data representing redundant information which may reduce the network efficiency. This study aims to reduce redundant data collection while deploying the minimum number of UAVs, minimizing energy consumption and maximizing VoI. We first formulate the general problem and solve it as a multi-objective optimization problem. We then decompose the problem into two sub-problems where wepropose real-time approaches including (1) data redundancy avoidance and VoI evaluation, and (2) dynamic UAV deployment and position adaptation. In the first problem, the proposed approach clusters SNs and prioritizes non-redundant data by assigning VoI, while neglecting redundant data. In the second, we consider optimized UAV position adaptation where we generated the problem as a multi-objective optimization problem and solved it as a mixed-integer linear programming problem with constraints related to UAV range, UAV steps, and time constraints. To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. The model considers VoI and energy constraints of the SNs, enhancing both efficiency and sustainability. The proposed approach outperforms other algorithms, demonstrating higher efficiency in terms of UAV deployment, served SN, VoI and energy consumption. |
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