Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance

• URL: http://arxiv.org/abs/2602.16738v1
• Date: Tue, 17 Feb 2026 22:45:43 GMT
• Title: Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance
• Authors: Rebin Saleh, Khanh Pham Dinh, Balázs Villányi, Truong-Son Hy,
• Abstract summary: Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources.<n>Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions.<n>We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers.
• Score: 5.571627005866756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge deployment. We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers. Edge agents perform lightweight feature extraction and pre-filtering; Fog agents execute diversified ensemble detection with dynamic consensus voting; and Cloud agents continuously optimize system policies via Proximal Policy Optimization (PPO) while maintaining asynchronous, non-blocking inference. The framework incorporates LLM-based response generation for explainability and federated knowledge aggregation for adaptive policy distribution. This architecture enables resource-aware specialization without sacrificing real-time performance or model interpretability. Empirical evaluation on two industrial benchmarks (Boiler Emulator and Wind Turbine) demonstrates that SEMAS achieves superior anomaly detection performance with exceptional stability under adaptation, sustains prediction accuracy across evolving operational contexts, and delivers substantial latency improvements enabling genuine real-time deployment. Ablation studies confirm that PPO-driven policy evolution, consensus voting, and federated aggregation each contribute materially to system effectiveness. These findings indicate that resource-aware, self-evolving 1multi-agent coordination is essential for production-ready industrial IoT predictive maintenance under strict latency and explainability constraints.

Related papers

• Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System [26.405948122941467]
  We present GEARS, a framework that reframes ranking optimization as an autonomous discovery process.<n>We show that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context.
  arXiv  Detail & Related papers  (2026-02-20T22:24:01Z)
• AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering [52.67783579040657]
  AceGRPO is a machine learning system that prioritizes tasks at the agent's learning frontier to maximize learning efficiency.<n>Our trained Ace-30B model achieves a 100% valid submission rate on MLE-Bench-Lite, approaches the performance of proprietary frontier models, and outperforms larger open-source baselines.
  arXiv  Detail & Related papers  (2026-02-08T10:55:03Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation [0.5417521241272645]
  The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains.<n>We propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction.
  arXiv  Detail & Related papers  (2025-12-22T12:59:48Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing [0.0]
  This paper presents a hybrid agentic AI and multi agent framework for a Prescriptive Maintenance use case.<n>The proposed framework adopts a layered architecture that consists of perception, preprocessing, analytics, and optimization layers.<n> Specialized agents autonomously handle schema discovery, intelligent feature analysis, model selection, and prescriptive optimization.<n>An initial proof of concept implementation is validated on two industrial manufacturing datasets.
  arXiv  Detail & Related papers  (2025-11-23T03:06:23Z)
• Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
  Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
  arXiv  Detail & Related papers  (2025-09-27T10:53:48Z)
• Cloud-Device Collaborative Agents for Sequential Recommendation [36.05863003744828]
  Large language models (LLMs) have enabled agent-based recommendation systems with strong semantic understanding and flexible reasoning capabilities.<n>LLMs offer powerful personalization, but they often suffer from privacy concerns, limited access to real-time signals, and scalability bottlenecks.<n>We propose a novel Cloud-Device collaborative framework for sequential Recommendation, powered by dual agents.
  arXiv  Detail & Related papers  (2025-09-01T15:28:11Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Adaptive Stream Processing on Edge Devices through Active Inference [5.5676731834895765]
  We present a novel Machine Learning paradigm based on Active Inference (AIF) AIF describes how the brain constantly predicts and evaluates sensory information to decrease long-term surprise. Our method guarantees full transparency on the decision making, making the interpretation of the results and the troubleshooting effortless.
  arXiv  Detail & Related papers  (2024-09-26T15:12:41Z)
• OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud [10.97507717758812]
  We propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module.<n> Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework.
  arXiv  Detail & Related papers  (2023-10-26T04:38:48Z)

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.