Tiny Machine-Learning Operations within Cyber-Physical Systems: a Field Study

• URL: http://arxiv.org/abs/2606.20952v1
• Date: Thu, 18 Jun 2026 21:34:01 GMT
• Title: Tiny Machine-Learning Operations within Cyber-Physical Systems: a Field Study
• Abstract summary: We report an end-to-end, knowledge-centered TinyMLOps pipeline that fuses domain physics, expert speculation, and sensor streams.<n>We evaluate it on 4.4 GB of data from two offshore-wind cable-trenching campaigns.<n>We distill engineering lessons and threats for trustworthy TinyMLOps in CPS, and release code and an annotated dataset to support validity.
• Score: 0.9645899225858455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine-Learning Operations (MLOps) is maturing into a software-engineering discipline, yet its tiny-scale variant (TinyMLOps)-targeting the resource-constrained microcontrollers embedded in cyber-physical systems (CPS)-remains poorly understood in industrial practice. Opaque models, noisy heterogeneous data, and tight memory budgets hinder adoption in safety-critical settings, where most decisions still rely on human experts. We report a field study of an end-to-end, knowledge-centered TinyMLOps pipeline that fuses domain physics, expert speculation, and sensor streams to deliver explainable, low-footprint models deployable on-device. The pipeline spans automated collection and cleaning of heterogeneous time series, knowledge-driven feature construction, interpretable regularized models, and rolling temporal cross-validation under concept drift. We evaluate it on 4.4 GB of data from two offshore-wind cable-trenching campaigns. The classifier anticipates harmful load peaks up to three minutes ahead at 0.84 AUC within a 32 kB footprint on an ARM Cortex-M4; an ablation shows that injecting prior knowledge halves false alarms and surfaces actionable operational rules. Replaying recommendations in operational dashboards indicates an 11% reduction in non-productive time. We distill engineering lessons and validity threats for trustworthy TinyMLOps in CPS, and release code and an annotated dataset to support reproducibility.

Related papers

• Test-Time Unlearning via Sparse Autoencoder [55.4290003355764]
  We propose ARIA, a test-time unlearning method that leaves model weights intact and gates access to unwanted knowledge only when generation enters a forget-related state.<n>ARIA improves the forget-retain trade-off over weight-based baselines across both a thinking model and an instruction model.
  arXiv  Detail & Related papers  (2026-09-14T18:58:09Z)
• TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection [0.6372261626436676]
  Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats.<n>Using the SPARTABurst model, we analyze the latency-accuracy trade-offs of TinyML-compatible classical models.<n>Results show Logistic Regression achieves only a 1% accuracy drop relative to Random Forest, making it an effective TinyML baseline for onboard autonomy.
  arXiv  Detail & Related papers  (2026-06-04T07:08:28Z)
• RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair [1.7118181664522618]
  Large language models (LLMs) inherently absorb harmful knowledge, misinformation, and personal data during pretraining on large-scale web corpora.<n>We introduce Interactive Machine Unlearning (IMU), a new paradigm in which users can instruct LLMs to forget targeted knowledge through natural language at inference time.<n>We develop RePAIR, a prompt-aware model repair framework comprising (i) a watchdog model for unlearning intent detection, (ii) a surgeon model for generating repair procedures, and (iii) a patient model whose parameters are updated autonomously.
  arXiv  Detail & Related papers  (2026-04-14T14:44:45Z)
• FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios [58.34124792457706]
  The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution.<n>Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets.<n>We first construct a high-quality dataset that combines realworld 2D images and 3D point clouds, annotated with fine-grained domain semantics.<n>We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely verification, structural surface inspection, and assembly verification, revealing significant performance gaps.
  arXiv  Detail & Related papers  (2026-04-08T12:23:27Z)
• Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data [0.13750624267664155]
  This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller.<n>Anomalies are detected using lightweight Z-Score thresholds, enabling interpretable and computationally efficient inference suitable for embedded deployment.<n>Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB and 63 KB Flash.
  arXiv  Detail & Related papers  (2026-03-28T20:15:50Z)
• Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking [0.0]
  steganographic collusion, where agents develop private protocols to evade monitoring, presents a critical AI safety threat.<n>Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels.<n>We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate.
  arXiv  Detail & Related papers  (2026-03-07T04:14:38Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning [58.620753467152376]
  In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices.<n>To prevent the eavesdroppers from collecting model and data information, a subset of devices can transmit deceptive signals.<n>We propose a soft actor-critic deep reinforcement learning framework with intrinsic curiosity module and cross-attention.
  arXiv  Detail & Related papers  (2025-07-09T22:53:23Z)
• Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning [2.66512000865131]
  We propose an approach for assurance monitoring of learning-enabled Cyber-Physical Systems. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition.
  arXiv  Detail & Related papers  (2021-10-07T00:21:45Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework [56.57225686288006]
  Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
  arXiv  Detail & Related papers  (2020-03-13T23:52:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.