在LoRa网络中的数据压缩:经典和尖端压缩算法的性能和能量权衡
Rafaella Laureano Dias1, Evandro César Vilas Boas1, Felipe A P de Figueiredo1
1Wireless and Artificial Intelligence Laboratory (WAI Lab) and the Critical Telecommunications and IoT Infrastructures Laboratory (CTIoT Lab), National Institute of Telecommunication (Inatel), Santa Rita do Sapucaí 37540-000, MG, Brazil.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
对于使用LoRa的节能物联网 (IoT) 网络,LZW压缩算法提供了最佳的节能. 先进的机器学习压缩机对于终端设备来说太耗电了.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 无线通信无线通信
背景情况:
- 物联网 (IoT) 设备需要节能通信,特别是在像LoRa这样的远程,低功耗网络中.
- 无线电通信是LoRa终端设备的主要能源消耗者,需要数据压缩以减少传输能量.
- 无损数据压缩可以减少数据包的大小和传输频率,从而节省能源.
研究的目的:
- 为LoRa网络全面评估经典和尖端的无损压缩算法.
- 评估数据压缩对LoRa终端设备的能源消耗,CPU负载和内存使用的影响.
- 为实际的LoRa应用确定最节能的压缩算法.
主要方法:
- 实验是在使用RFM95WLoRa模块和INA219传感器的Raspberry Pi 5上进行的.
- 对各种压缩算法 (Huffman,LZW,BSC,CMIX,PAQ8PX,GMIX,LSTM-compress) 进行实时功耗,CPU负载和内存使用量测量.
- 对每个算法进行了能源效率,压缩比和元数据开销分析.
主要成果:
- 伦佩尔-齐夫-韦尔奇 (LZW) 算法证明了最高的能效,将LoRa传输能量降低了高达7.41%.
- 基于先进的机器学习 (ML) 的算法 (CMIX,PAQ8PX) 实现了更高的压缩比,但由于高计算和内存开销,导致负能量增长.
- 元数据的开销影响了有效载荷的效率,特别是对于小数据包.
结论:
- 对于资源有限的LoRa端节点,LZW是最实用和最节能的压缩选择.
- 现代压缩机,包括基于ML的压缩机,更适合用于具有更大的计算资源的网关或边缘服务器.
- 实验框架的开源实现可用于进一步的研究和开发.
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