Yoon Ikjune
Division of AI Computer Science and Engineering, Kyonggi University, Suwon-si 16227, Republic of Korea.
Sensors (Basel). 2025 Apr 18;25(8):2559. doi: 10.3390/s25082559.
Wireless Sensor Networks (WSNs) are widely used for environmental data collection; however, their reliance on battery power significantly limits network longevity. While energy harvesting technologies provide a sustainable power solution, conventional approaches often fail to efficiently utilize surplus energy, leading to performance constraints. This paper proposes an energy-efficient dual-mode data collection scheme that integrates Long Range Wide Area Network (LoRaWAN) and Bluetooth Low Energy (BLE) in an energy-harvesting WSN environment. The proposed method dynamically adjusts sensing intervals based on harvested energy predictions and reserves energy for urgent data transmissions. Urgent messages are transmitted via BLE using multi-hop routing with redundant paths to ensure reliability, while periodic environmental data is transmitted over LoRaWAN in a single hop to optimize energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances data collection efficiency and improves urgent message delivery reliability compared to existing approaches. Future work will focus on optimizing energy consumption for redundant urgent transmissions and integrating error correction mechanisms to further enhance transmission reliability.
无线传感器网络（WSN）被广泛用于环境数据收集；然而，它们对电池供电的依赖严重限制了网络寿命。虽然能量收集技术提供了一种可持续的电源解决方案，但传统方法往往无法有效利用剩余能量，从而导致性能受限。本文提出了一种节能双模式数据收集方案，该方案在能量收集无线传感器网络环境中集成了长距离广域网（LoRaWAN）和低功耗蓝牙（BLE）。所提出的方法基于收集到的能量预测动态调整传感间隔，并为紧急数据传输预留能量。紧急消息通过BLE使用具有冗余路径的多跳路由进行传输，以确保可靠性，而周期性环境数据则通过LoRaWAN单跳传输，以优化能量效率。仿真结果表明，与现有方法相比，所提出的方案显著提高了数据收集效率，并提高了紧急消息传递的可靠性。未来的工作将集中于优化冗余紧急传输的能耗，并集成纠错机制以进一步提高传输可靠性。
