arXiv AI

Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents

arXiv:2607. 04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization.

Hugging Face Trending Papers
Jul 14

Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration

The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework. The proposed architecture consists of cloud, edge/fog, and physical IoT layers connected through autonomous AI agents that perceive, reason, coordinate, and actuate across distributed cyber-physical environments.

arXiv Machine Learning
1d ago

Towards a Cloud Fog Edge System for Smart Building

The article outlines a vision and recent progress toward a decentralized system that learns from real‑time building data, treating the building itself as a data center to enhance privacy and reduce dependence on external clouds. It introduces a lightweight, Kubernetes‑like orchestration framework for deploying AI services on low‑power microcontrollers, such as those in the Arduino ecosystem, enabling in‑situ learning on sensors. The work also presents experimental results for new online learning algorithms and proposes a cloud‑fog‑edge architecture using KOptim and FIWARE components.

By Christophe C\'erin, Mamadou Sow, Fr\'ed\'eric Andr\`es
arXiv Machine Learning
Jul 28

Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs

arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.

By Dawen Jiang, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Wei Xiang, Jiong Jin
arXiv AI
Aug 7

When Agentic AI Meets Integrated Sensing and Communication

arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.

By Kai Li, Conggai Li, Sarah Ali Siddiqui, Syed Sohail Ahmed, Xin Yuan, Shenghong Li, Wei Ni
arXiv AI
Aug 28

SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum

The paper introduces a SAREF-compliant ontology designed to represent distributed AI workflows across edge, fog, and cloud environments. It extends the SAREF4SYST ontology with concepts for AI pipelines, executable jobs, resources, deployment constraints, and communication links, creating a unified semantic model for both AI workflows and heterogeneous infrastructures. Evaluation through smart‑grid energy service scenarios and competency questions demonstrates successful deployment, reasoning, and workload adaptation, achieving 90‑100% deployment success and sub‑80 ms orchestration times.

By Viorica Rozina Chifu, Tudor Cioara, Vasile Ofrim, Liana Toderean, Ionut Anghel, Laura Daniele, Cornelis Bouter
arXiv AI
Aug 3

OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems

arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.

By Konstantinos I. Roumeliotis, Ranjan Sapkota