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To address issues of poor information transmission delay, packet loss rate, and throughput rate in smart grids, this study proposes a collaborative optimization framework based on edge computing and adaptive compressed sensing. An improved LZW algorithm is deployed at terminals for data compression. The K-means++ clustering algorithm determines edge node deployment locations, enabling local data preprocessing. A QoS-aware transmission protocol is designed, incorporating a priority queue mechanism to control the delay of critical telemetry data. Experimental verification shows that this framework achieves an end-to-end delay of 47.3 → 76.2 ms, a throughput rate attenuation of only 8.3%, and a packet loss rate within 2.4%, providing key technical support for the large-scale expansion of smart grids.

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Data Acquisition and Information Transmission Optimization Technology in Smart Grids

  • 会议论文

  • 发表日期：

    2026年

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  • Renyi Qi,
  • Ming Cai,
  • Yijie Zhi

摘要

To address issues of poor information transmission delay, packet loss rate, and throughput rate in smart grids, this study proposes a collaborative optimization framework based on edge computing and adaptive compressed sensing. An improved LZW algorithm is deployed at terminals for data compression. The K-means++ clustering algorithm determines edge node deployment locations, enabling local data preprocessing. A QoS-aware transmission protocol is designed, incorporating a priority queue mechanism to control the delay of critical telemetry data. Experimental verification shows that this framework achieves an end-to-end delay of 47.3 → 76.2 ms, a throughput rate attenuation of only 8.3%, and a packet loss rate within 2.4%, providing key technical support for the large-scale expansion of smart grids.