Aug 2026· Technologies· Vol 14, pp. 476· 0 citations· 61 references
TL;DR
The Adaptivecoreset Selection Engine (ACS-Engine), a unified framework for adaptive, differentiable, and resource-aware coreset selection on streaming IoT data, is proposed, a unified framework for adaptive, differentiable, and resource-aware coreset selection on streaming IoT data.
Abstract
The rapid growth of Internet of Things (IoT) devices generates high-dimensional, high-velocity data streams that demand real-time machine learning (ML) inference under strict hardware constraints. We propose the Adaptivecoreset Selection Engine (ACS-Engine), a unified framework for adaptive, differentiable, and resource-aware coreset selection on streaming IoT data. ACS-Engine introduces three tightly integrated innovations: (i) Differentiable Greedy Sampling (DGS), which relaxes discrete subset selection via Gumbel-Softmax reparameterization to enable end-to-end gradient-based optimization; (ii) Entropy-Aware Regularization (EAR), which promotes coreset diversity and provides implicit concept drift detection through a self-calibrating entropy threshold; and (iii) Resource-Aware Memory Management (RAMM), which dynamically adjusts the target coreset size based on real-time hardware telemetry—available memory, CPU utilization, remaining energy, and sampling frequency. Evaluated on eight real-world IoT datasets spanning three heterogeneous edge platforms, ACS-Engine achieves 15× memory reduction and a 20% energy efficiency improvement while retaining 98% of full-dataset accuracy, with a per-sample latency of 2 ms that satisfies real-time edge deployment requirements.
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.
J. Kangethe, Ifrat Ikhtear Uddin, Longwei Wang· 0 citations
Results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
The exponential expansion of the Internet of Things (IoT) has created a complex threat landscape that challenges traditional intrusion detection systems (IDS), particularly on edge devices with stringent computational and memory budgets. Beyond lightweight inference, practical IoT security increasingly requires on-device learning and frequent retraining so that models can adapt to evolving traffic patterns and emerging attacks; however, repeatedly training on high-dimensional data and large traffic corpora remains prohibitively expensive for resource-constrained devices. Existing lightweight IDS solutions often treat dimensionality reduction and data pruning as isolated tasks, leading to suboptimal representations and a lack of theoretical guarantees. To address this, we propose the Sequential Submodular Feature-Sample Selection (SSFSS) framework, a theoretically grounded approach that sequentially optimizes the feature and sample spaces to reduce training cost while preserving detection fidelity. First, we introduce Label-Aware Coreset Greedy-Based Feature Selection (LA-CGFS), which formulates feature selection as a label-conditioned facility location problem. This stage maximizes coverage across class manifolds, preserving discriminative power for minority attack classes without relying on synthetic oversampling. Second, we employ a geometry-aware coreset selection strategy that minimizes geometric coverage error in the reduced feature space. By leveraging the submodular property of diminishing returns, SSFSS provides a proven \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1-1/e)$$\end{document} approximation guarantee to the optimal subset. Extensive evaluation on the RT-IoT2022, Edge-IIoTset, and CICIoT2023 datasets demonstrates that SSFSS reduces the feature space to 20 features and the training set to as little as 5% of the original samples, achieving average training speedups of up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70\times $$\end{document} and up to a 96% reduction in peak training RAM; even when the one-time coreset-construction cost is included, the end-to-end pipeline remains faster than a single full-data fit. Across four differentiable empirical-risk-minimization classifiers (kernel logistic regression, kernel SVM, kernel ridge, and softmax regression), the best reduced model retains 97.5–99.5% accuracy at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\%$$\end{document} budget, and the weighted coreset improves macro-F1 and balanced accuracy on the imbalanced benchmarks. Supported by the reported results, we position the proposed SSFSS framework as an efficient update-aware preprocessing framework whose measured speedups and RAM consumption motivate deployment on resource-constrained edge hardware.
Mohammed Nagah Amr, Ahmed S. Elliethy, T. Mekkawy et al.· Scientific Reports· 0 citations
The extensive deployment of battery-powered and resource-constrained edge devices makes energy efficiency a major challenge in Internet of Things (IoT) systems. Accurate energy prediction is important to enable intelligent energy management. However, traditional machine learning models are usually computationally expensive and unsuitable for micro-controller based platforms. In this paper, we present a TinyML-based energy prediction framework for low-power IoT edge devices. The proposed approach employs lightweight machine learning models which are optimized for ultra-low memory and computation footprints, but still retain acceptable prediction accuracy. We collect energy consumption data from a real IoT testbed, and train and evaluate several TinyML compatible models. The experimental results show that the proposed TinyML-based predictor can provide reliable energy estimation with low inference latency and low memory overhead, and thus can be deployed on resource-constrained IoT devices. This work lays a fundamental foundation for intelligent and adaptive energy management in future IoT systems.
Sandeep Kumar Rawat, Neha Tuli· International Journal of Inn...· 0 citations
This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization, and proposes a distributed learning framework that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy.
Venkatesh Iyer· International Journal of App...· 0 citations
Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%.
Shuo He, He-Yang Wei, Congxian Bi et al.· Electronics· 0 citations