The paper introduces FM4NILM, a prompt‑programmable foundation model that estimates the power usage of any requested appliance from whole‑home meter data, a natural‑language description, and optional activation examples. Trained on 645k sequences from seven public datasets, the lightweight transformer achieves competitive performance across twelve appliance requests, outperforming seven appliance‑specific baselines on key metrics such as event F1 and active‑window MAE. The model’s design allows new appliance coverage to be added via prompts and examples rather than building separate specialist networks.
By Xudong Wang, Jiacheng Cui, Junyu Xue, Tongxin Li, Guoming Tang
The paper introduces a black-box defense strategy for smart meter data that uses a proxy-guided hierarchical reinforcement learning framework to generate battery-based load-shaping policies. These policies inject realistic yet misleading appliance-level signatures into aggregate power signals, disrupting non-intrusive load monitoring attacks. Experiments on UK-DALE and REDD datasets show significant increases in appliance-level reconstruction error and reductions in attacker F1 scores across multiple unseen NILM models.
By Ruichang Zhang, Mustafa A. Mustafa
The paper introduces a new NILM approach that uses label‑preserving aggregate recomposition and prediction consistency to improve appliance‑level power estimation. By recomposing aggregate windows with only the residual background changed, the method preserves target appliance signals while exposing a new supervisory signal. Experiments on REDD, UK‑DALE, and REFIT show reduced mean absolute error for multiple appliances without adding inference‑time complexity.
By Jiangfeng Liu, Yanfang Fan
The paper surveys energy-aware TinyML research, mapping models, learning approaches, and energy-evaluation methods. It classifies influencing factors such as model architecture, hardware, communication, numerical precision, and input characteristics, and organizes optimization strategies into compression, hardware-aware design, system/communication, and data/processing categories. The study notes limited standardization in experimental platforms and reporting, hindering reproducibility and comparison.
By Clariele Almeida, Renan Beserra, Rafael Moura, Danilo Ara\'ujo, Bruno Nogueira, S\'ergio Chevtchenko, Ermeson Andrade
Ireland’s smart metering programme records electricity use at 30‑minute intervals, which is too coarse to capture domestic appliance use. The authors present a label‑free disaggregation system that splits usage data into nine appliance categories by combining event detection for high‑power loads with questionnaire‑guided estimation. Evaluated on four datasets—including a large Irish smart‑meter dataset of over 4,800 years of use—the hybrid method achieves the lowest whole‑decomposition error and better month‑level performance than two independently developed systems.
By Achal Nanjundamurthy, Rupam Misra, Suzanne Little, Alan F. Smeaton
The paper proposes a holistic architectural and algorithmic framework for sustainable cloud‑fog‑edge systems in smart buildings, emphasizing real‑time responsiveness and reduced environmental impact. It validates resilient data acquisition using affordable Arduino hardware and secure persistence solutions, introduces the lightweight KOptim orchestration framework for dynamic SLA management, and adapts the Generalized Hebbian Algorithm for PCA on low‑power microcontrollers with an iterative learning strategy that preserves data diversity. Experimental results on high‑dimensional sensor data demonstrate that this approach converges to quality comparable to full‑memory processing while operating within strict memory constraints.
By Mamadou Sow, Christophe Cerin
arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.
By Bohua Zou, Nian Liu, Binqi Sun, Matteo Mascherin, Debayan Roy, Yutao Liu, Yu Peng, Ning Jia, Haibo Chen
The paper introduces a cost‑aware hybrid approach that uses small, open‑source language models to classify smart data models (SDMs) in Internet of Things (IoT) environments. It benchmarks general‑purpose, reasoning‑specialized, and code‑specialized models across domain‑specific datasets, highlighting their suitability for edge devices with limited resources. The study also compares these lightweight models to large language models and near‑zero‑cost baselines such as TF‑IDF and a lightweight sentence encoder to demonstrate practical performance gains.
By Cristian Martella, Angelo Martella, Antonella Longo, Motaz Saad
arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).
By Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler
The paper presents a rapid pipeline for training and deploying machine‑learning models on the WeBe Band, a wrist‑worn wearable device. It automates the creation of hardware‑efficient models, integrates with the Piccolo AI ecosystem, and supports OTA deployment while profiling latency and memory usage. Experimental results show trade‑offs between classical models and lightweight neural networks for real‑time performance on a microcontroller.
By Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr
arXiv:2512. 22287v3 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier.
By Zikun Guo, Adeyinka. P. Adedigba, Rammohan Mallipeddi
The paper introduces a lightweight AI framework for vibration-based condition monitoring of consumer rotating devices. It transforms 1‑D vibration signals into 2‑D time‑frequency maps using continuous wavelet transform and Hilbert‑Huang transform, then applies compact deep learning models such as MobileNetV3‑Small, ConvNeXt‑Tiny, and YOLOv11‑cls. Experiments on public bearing datasets show that CWT with YOLOv11‑cls delivers high diagnostic accuracy, while MobileNetV3‑Small offers a low‑latency alternative suitable for edge deployment, and the study emphasizes the need to avoid data leakage in model evaluation.
By Po-Heng Chou, Jen-Yu Chiu, Chun-Yu Yeh, Wei-Lung Mao, Walid Saad