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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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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.

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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.

关键词:
这就是为什么物联网是物联网物联网.洛拉洛拉是什么意思数据压缩数据压缩.能源效率是指能效的能源效率.机器学习是机器学习.

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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.