Scholarlink

<p>The data-intensive nature of the Internet of Things (IoT) significantly challenges conventional communication systems. While over-the-air computation integrates communication and computation to reduce data aggregation burdens, it requires strict synchronization of transmitted signals, increasing system overhead. Here, we report an optical in-sensor wireless data aggregation paradigm that exploits photocarrier trapping in defect-dominated persistent photoconductance sensors to reduce dependence on synchronization constraints. Within a single communication window, the system reliably aggregates incoherent optical signals with relative timing offsets exceeding the signal duration by up to 120%, while achieving computation errors below 1.5% normalized mean squared error. This process achieves unbiased integration with pulsed low-frequency readout, indicating its potential for ultra-low power operation. A distributed temperature prediction model based on this paradigm shows faster convergence and competitive accuracy with a simplified architecture, demonstrating effectiveness in distributed edge sensing scenarios. This work provides a promising hardware implementation for low-power data aggregation in large-scale IoT edge networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Phase-independent wireless data aggregation via optical in-sensor computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

查看全文
  • Shuyi Sun,
  • Zexi Lu,
  • Yao Wang,
  • Zihui Liu,
  • Zhuang Mao,
  • Qi Shi,
  • Fengzhi Wang,
  • Wei Wang,
  • Jie Jiang,
  • Bin Lu,
  • Haiping He,
  • Xinhua Pan,
  • Haoliang Qian,
  • Zhizhen Ye

摘要

The data-intensive nature of the Internet of Things (IoT) significantly challenges conventional communication systems. While over-the-air computation integrates communication and computation to reduce data aggregation burdens, it requires strict synchronization of transmitted signals, increasing system overhead. Here, we report an optical in-sensor wireless data aggregation paradigm that exploits photocarrier trapping in defect-dominated persistent photoconductance sensors to reduce dependence on synchronization constraints. Within a single communication window, the system reliably aggregates incoherent optical signals with relative timing offsets exceeding the signal duration by up to 120%, while achieving computation errors below 1.5% normalized mean squared error. This process achieves unbiased integration with pulsed low-frequency readout, indicating its potential for ultra-low power operation. A distributed temperature prediction model based on this paradigm shows faster convergence and competitive accuracy with a simplified architecture, demonstrating effectiveness in distributed edge sensing scenarios. This work provides a promising hardware implementation for low-power data aggregation in large-scale IoT edge networks.