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Data Compression in LoRa Networks: Performance and Energy Trade-Offs of Classical and Cutting-Edge Compression

Rafaella Laureano Dias1, Evandro César Vilas Boas1, Felipe A P de Figueiredo1

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This summary is machine-generated.

For energy-efficient Internet of Things (IoT) networks using LoRa, the LZW compression algorithm offers the best energy savings. Advanced machine learning compressors are too power-intensive for end devices.

Keywords:
IoTLoRadata compressionenergy efficiencymachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Internet of Things (IoT) devices require energy-efficient communication, especially in long-range, low-power networks like LoRa.
  • Radio communication is the primary energy consumer in LoRa end devices, necessitating data compression to reduce transmission energy.
  • Lossless data compression can decrease packet size and transmission frequency, thereby saving energy.

Purpose of the Study:

  • To comprehensively evaluate classical and cutting-edge lossless compression algorithms for LoRa networks.
  • To assess the impact of data compression on energy consumption, CPU load, and memory usage in LoRa end devices.
  • To determine the most energy-efficient compression algorithm for practical LoRa applications.

Main Methods:

  • Experiments were conducted on a Raspberry Pi 5 with an RFM95W LoRa module and INA219 sensors.
  • Real-time power consumption, CPU load, and memory usage were measured for various compression algorithms (Huffman, LZW, BSC, CMIX, PAQ8PX, GMIX, LSTM-compress).
  • Energy efficiency, compression ratio, and metadata overhead were analyzed for each algorithm.

Main Results:

  • The Lempel-Ziv-Welch (LZW) algorithm demonstrated the highest energy efficiency, reducing LoRa transmission energy by up to 7.41%.
  • Advanced machine learning (ML)-based algorithms (CMIX, PAQ8PX) achieved higher compression ratios but resulted in negative energy gains due to high computational and memory overhead.
  • Metadata overheads impacted payload efficiency, especially for small data packets.

Conclusions:

  • LZW is the most practical and energy-efficient compression choice for resource-constrained LoRa end nodes.
  • Modern compressors, including ML-based ones, are better suited for gateways or edge servers with greater computational resources.
  • An open-source implementation of the experimental framework is available for further research and development.