Internet of Things

Also known as IoT

The Internet of Things studies how connected devices sense and act in homes, factories, cities, and other environments, often with limited power, memory, and bandwidth. Researchers develop embedded and edge AI, federated learning, wireless protocols, and cloud-edge systems to analyze sensor data, coordinate devices, and reduce communication or energy costs. Security and privacy work includes intrusion detection, device identification, trust management, and learning methods that keep raw data local; newer systems also explore language-model agents for smart-home control and IoT development. A central challenge is making these methods reliable across heterogeneous devices and changing conditions, since strong benchmark results do not always transfer to robust, secure real-world deployments.

Latest papers

107 papers12 papers in the last four weeks, up 20% on the four weeks before. 0.1% of all new papers.

FRIEND: Federated Learning for Joint Optimization of multi-RIS Configuration and Eavesdropper Intelligent Detection in B5G Networks
Mar 11, 2026Maria Lamprini A. Bartsioka, Ioannis A. Bartsiokas, Anastasios K. Papazafeiropoulos +3Industrial Internet of Things Wireless Communications
Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou str, Zografou 15780, Athens, Greece · Communications and Intelligent Systems Research Group, University of Hertfordshire, AL10 9AB Hatfield, U.K. · Intelligent Communications and Broadband Networks Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou str, Zografou 15780, Athens, Greece
Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation
Mar 8, 2026Kenechi Omeke, Attai Abubakar, Michael Mollel +3Internet of Things Wireless Communications
James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
Tureis: Transformer-based Unified Resilience for IoT Devices in Smart Homes
Mar 5, 2026Alireza Borhani, Vafa Andalibi, Bahar AsgariFault Detection Fault Localization
University of Maryland, College Park, MD, USA · Google, Manhattan, NY, USA
S5-SHB Agent: Society 5.0 enabled Multi-model Agentic Blockchain Framework for Smart Home
Mar 5, 2026Janani Rangila, Akila Siriweera, Incheon Paik +3AI Agent Governance Internet of Things
KD University, Colombo, Sri Lanka · University of Aizu, Fukushima, Japan
Unsupervised Surrogate-Assisted Synthesis of Free-Form Planar Antenna Topologies for IoT Applications
Mar 4, 2026Khadijeh Askaripour, Adrian Bekasiewicz, Slawomir KozielTopology Optimization Surrogate-Assisted Optimization
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland · Department of Engineering, Reykjavik University, Reykjavik, Iceland
S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation
Mar 2, 2026Akila Siriweera, Janani Rangila, Keitaro Naruse +2Internet of Things LLM Agent Orchestration
The University of Aizu, Aizu Wakamatsu, Fukushima, Japan · The KD University, Rathmalana, Colombo, Sri Lanka
Unsupervised Baseline Clustering and Incremental Adaptation for IoT Device Traffic Profiling
Feb 27, 2026Sean M. Alderman, John D. HastingsInternet of Things Clustering
The Beacom College of Computer & Cyber Sciences, Dakota State University, Madison, SD, USA
ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution
Feb 26, 2026Srikumar NayakExplainable Intrusion Detection IoT Intrusion Detection
Incedo Inc, USA
Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices
Feb 26, 2026Soroosh Miri, Sepehr Abolhasani, Shahrokh Farahmand +2Deep Joint Source-Channel Coding Spatial Attention
School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran · Great Bay University, Dongguan 523000, China
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
Feb 24, 2026Qingsong Zou, Zhi Yan, Zhiyao Xu +3Anomaly Detection LLM Evaluation
Tsinghua Shenzhen International Graduate School Shenzhen, China · Jilin University Jilin, China · The Chinese University of Hong Kong Hong Kong, China +1
A Framework for Hybrid Collective Inference in Distributed Sensor Networks
Feb 19, 2026Andrew Nash, Dirk Pesch, Krishnendu GuhaDistributed Inference Multi-Agent Reasoning
School of Computer Science and IT, University College Cork, Ireland.
Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT
Feb 18, 2026Amirmohammad Pasdar, Shabnam Kasra Kermanshahi, Nour Moustafa +1Network Intrusion Detection Internet of Things
School of Computing and Information Systems, The University of Melbourne, VIC 3010, Australia · School of Systems & Computing, University of New South Wales, ACT 2612, Australia
A Scalable Multi-Task Model for Virtual Sensors
Jan 28, 2026Leon Götz, Lars Frederik Peiss, Erik Sauer +4Virtual Sensing Multi-Task Learning
Volkswagen AG · Technical University of Munich · Helmholtz AI
Energy Overhead in Sensor Nodes: Embedded Machine Learning and Data Offloading via Low Power Wide Area Networks
Oct 28, 2025Benjamin Karic, Nina Herrmann, Jan Stenkamp +3Internet of Things Energy-Efficient ML
University of Münster · re:edu GmbH & Co. KG
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Sep 24, 2025Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti +1Internet of Things LLMs for Cybersecurity
New York University, New York, NY, USA · Google, Mountain View, CA, USA
Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey
Apr 10, 2025Fabrizio Mangione, Claudio Savaglio, Giancarlo FortinoInternet of Things
DIMES Dept., University Of Calabria, Rende, Italy
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
Dec 9, 2024Jiechao Gao, Yuangang Li, Jie Wang +3Hierarchical Federated Learning Internet of Things
University of Virginia Charlottesville, VA, USA

Mar 11, 2026J/K move · Enter open · S save

FRIEND: Federated Learning for Joint Optimization of multi-RIS Configuration and Eavesdropper Intelligent Detection in B5G Networks

Maria Lamprini A. Bartsioka, Ioannis A. Bartsiokas, Anastasios K. Papazafeiropoulos, Maria A. Seimeni+2

Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou str, Zografou 15780, Athens, Greece · Communications and Intelligent Systems Research Group, University of Hertfordshire, AL10 9AB Hatfield, U.K. · Intelligent Communications and Broadband Networks Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou str, Zografou 15780, Athens, Greece

Industrial Internet of Things Wireless Communications Internet of Things Reconfigurable Intelligent Surfaces Federated Learning

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As wireless systems evolve toward Beyond 5G (B5G), the adoption of cell-free (CF) millimeter-wave (mmWave) architectures combined with Reconfigurable Intelligent Surfaces (RIS) is emerging as a key enabler for ultra-reliable, high-capacity, scalable, and secure Industrial Internet of Things (IIoT) communications. However, safeguarding these complex and distributed environments against eavesdropping remains a critical challenge, particularly when conventional security mechanisms struggle to overcome scalability, and latency constraints. In this paper, a novel framework for detecting malicious users in RIS-enhanced cell-free mmWave networks using Federated Learning (FL) is presented. The envisioned setup features multiple access points (APs) operating without traditional cell boundaries, assisted by RIS nodes to dynamically shape the wireless propagation environment. Edge devices collaboratively train a Deep Convolutional Neural Network (DCNN) on locally observed Channel State Information (CSI), eliminating the need for raw data exchange. Moreover, an early-exit mechanism is incorporated in that model to jointly satisfy computational complexity requirements. Performance evaluation indicates that the integration of FL and multi-RIS coordination improves approximately 30% the achieved secrecy rate (SR) compared to baseline non-RIS-assisted methods while maintaining near-optimal detection accuracy levels. This work establishes a distributed, privacy-preserving approach to physical layer eavesdropping detection tailored for next-generation IIoT deployments.