Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks

Abstract

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.