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Adaptive Transmission Strategies for Energy-Efficient Long-Term Outdoor IoT Monitoring |
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| รหัสดีโอไอ | |
| Creator | Jaratpong Tepmanee |
| Title | Adaptive Transmission Strategies for Energy-Efficient Long-Term Outdoor IoT Monitoring |
| Contributor | Dumrongsak Wongta, Satawat Muangchuen, Kittikom Nontprasat |
| Publisher | Faculty of Engineering and Industrial Technology, Kalasin University |
| Publication Year | 2569 |
| Journal Title | วารสารวิศวกรรมและเทคโนโลยีอุตสาหกรรม มหาวิทยาลัยกาฬสินธุ์ |
| Journal Vol. | 4 |
| Journal No. | 3 |
| Page no. | 1-21 |
| Keyword | Internet of Things, Adaptive Sampling, Data Reduction, PM2.5 Monitoring, Energy-Efficient Transmission, Environmental Sensing, ESP32, Edge Computing |
| URL Website | https://ph03.tci-thaijo.org/index.php/JEIT |
| Website title | วารสารวิศวกรรมและเทคโนโลยีอุตสาหกรรม มหาวิทยาลัยกาฬสินธุ์ |
| ISSN | ISSN 2985-0274 (Print),ISSN 2985-0282 (Online) |
| Abstract | Continuous high-resolution data transmission in Internet of Things (IoT)-based environmental monitoring systems leads to significant communication overhead and energy consumption, particularly in long-term outdoor deployments. This study aims to evaluate and compare energy-efficient data transmission strategies for long-term outdoor IoT environmental monitoring systems under real-world conditions. A system-level evaluation was conducted using a real-world environmental sensing platform deployed in Chiang Rai, Thailand. The system, built on an ESP32 microcontroller with SHT20 and PMS3003 sensors, collected temperature, humidity, and PM2.5 data at 30-second intervals from March 2024 to May 2025, resulting in 1,048,406 transmission events under the baseline configuration. Two transmission reduction strategies were evaluated: fixed downsampling and adaptive sampling based on signal variability. Performance was assessed using Data Reduction Ratio (DRR) and Event Preservation Ratio (EPR), including both PM-only and multi-parameter event detection. The fixed downsampling approach achieved the highest data reduction (90.00%) but preserved only 15.91% of multi-parameter environmental events. In contrast, adaptive sampling reduced transmission by 78.89% while preserving 56.76% of combined environmental events. The results demonstrate that maximizing transmission reduction alone is not suitable for dynamic environmental monitoring. Variability-aware adaptive transmission provides a more balanced trade-off between energy efficiency and event preservation. This study proposes a practical evaluation framework for designing energy-constrained IoT monitoring systems under real long-term outdoor conditions. |
