Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission
This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/6/578 |
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| author | Lulu Jing Hai Wang Zhen Qin Peng Zhu |
| author_facet | Lulu Jing Hai Wang Zhen Qin Peng Zhu |
| author_sort | Lulu Jing |
| collection | DOAJ |
| description | This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines. |
| format | Article |
| id | doaj-art-2fafa36b10d5477883a18f5b356f05b1 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-2fafa36b10d5477883a18f5b356f05b12025-08-20T03:27:14ZengMDPI AGEntropy1099-43002025-05-0127657810.3390/e27060578Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data TransmissionLulu Jing0Hai Wang1Zhen Qin2Peng Zhu3College of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, ChinaCollege of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, ChinaDepartment of Information and Communication, Noncommissioned Officer Academy of PAP, Hangzhou 310000, ChinaCollege of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, ChinaThis paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines.https://www.mdpi.com/1099-4300/27/6/578unmanned aerial vehicle relayage of informationrestless multi-armed banditl-conditional cross-entropygain-index-based policy |
| spellingShingle | Lulu Jing Hai Wang Zhen Qin Peng Zhu Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission Entropy unmanned aerial vehicle relay age of information restless multi-armed bandit l-conditional cross-entropy gain-index-based policy |
| title | Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission |
| title_full | Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission |
| title_fullStr | Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission |
| title_full_unstemmed | Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission |
| title_short | Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission |
| title_sort | entropy based age aware scheduling strategy for uav assisted iot data transmission |
| topic | unmanned aerial vehicle relay age of information restless multi-armed bandit l-conditional cross-entropy gain-index-based policy |
| url | https://www.mdpi.com/1099-4300/27/6/578 |
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