学习传输：面向节能物联网网络的波动感知预测通信
Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks
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中文总结 AI 辅助
研究面向节能物联网网络的波动感知预测通信，提出ADAPTIVEML框架及扩展ADAPTIVEML-RLS，通过轻量级机器学习预测器和自适应阈值决策通信，实验表明其能有效减少传输、降低重建误差，提升物联网网络能效和适应性。
中文摘要 AI 辅助
通信是物联网网络中能源消耗的主要来源，然而许多传感测量具有很强的时间相关性，给接收器提供的新信息很少。本文介绍了ADAPTIVEML，一种波动感知预测通信框架，使物联网设备能够智能决定何时需要通信。每个传感器维护一个轻量级机器学习预测器，仅在预测残差超过与本地信号波动成比例的自适应阈值时才传输。通过使用信号变异性的滚动估计对预测误差进行归一化，所提出的传输策略可自动适应不断变化的环境条件、季节变化和特定部署动态，无需手动调整阈值。为解决长期非平稳性，还提出了ADAPTIVEML-RLS，一种基于递归最小二乘法（RLS）和指数遗忘的在线学习扩展，允许持续适应传感器漂移和不断变化的信号特征。在三个异构真实世界数据集上进行了广泛实验，包含来自户外环境监测、室内无线传感器网络和城市空气质量传感的超过240万个传感器观测值。与六个代表性基线相比，ADAPTIVEML实现了高达94.7%的传输减少，同时保持0.352°C的重建误差。ADAPTIVEML-RLS在漂移条件下进一步将重建误差降低12 - 18%，同时保持传输减少率高于93%。这些结果证明了波动感知预测通信对节能和自适应物联网网络的有效性。
英文摘要
Communication is the dominant source of energy consumption in Internet-of-Things (IoT) networks, yet many sensed measurements exhibit strong temporal correlations and provide little new information to the receiver. This paper introduces \textsc{ADAPTIVEML}, a volatility-aware predictive communication framework that enables IoT devices to intelligently decide when communication is necessary. Each sensor maintains a lightweight machine learning predictor and transmits only when the prediction residual exceeds an adaptive threshold proportional to the local signal volatility. By normalizing prediction errors using a rolling estimate of signal variability, the proposed transmission policy automatically adapts to changing environmental conditions, seasonal variations, and deployment-specific dynamics without manual threshold tuning. To address long-term non-stationarity, we further propose \textsc{ADAPTIVEML-RLS}, an online learning extension based on Recursive Least Squares (RLS) with exponential forgetting, allowing continuous adaptation to sensor drift and evolving signal characteristics. Extensive experiments are conducted on three heterogeneous real-world datasets comprising more than 2.4 million sensor observations from outdoor environmental monitoring, indoor wireless sensor networks, and urban air-quality sensing. Compared with six representative baselines, including periodic transmission, static-threshold suppression, ARIMA, Kalman filtering, EMA, and LMS filtering, \textsc{ADAPTIVEML} achieves up to 94.7\% transmission reduction while maintaining a reconstruction error of 0.352$^\circ$C. \textsc{ADAPTIVEML-RLS} further reduces reconstruction error by 12--18\% under drift conditions while preserving transmission reduction above 93\%. These results demonstrate the effectiveness of volatility-aware predictive communication for energy-efficient and adaptive IoT networks.
