Internet of Thing
The Internet of Things (IoT) involves connecting billions of devices to collect and analyze data, aiming to improve efficiency and decision-making across various sectors. Current research heavily focuses on enhancing IoT security through machine learning (ML) models like deep neural networks (CNNs, LSTMs, Transformers), federated learning, and the integration of large language models (LLMs) for improved anomaly detection and attack prediction. These advancements are crucial for addressing the growing concerns of data privacy, security vulnerabilities, and resource constraints within increasingly complex IoT networks, impacting fields from smart cities to healthcare.
Papers
On Fulfilling the Exigent Need for Automating and Modernizing Logistics Infrastructure in India: Enabling AI-based Integration, Digitalization, and Smart Automation of Industrial Parks and Robotic Warehouses
Shaurya Shriyam, Prashant Palkar, Amber Srivastava
A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping
Sérgio F. Chevtchenko, Elisson da Silva Rocha, Monalisa Cristina Moura Dos Santos, Ricardo Lins Mota, Diego Moura Vieira, Ermeson Carneiro de Andrade, Danilo Ricardo Barbosa de Araújo
RFID-Assisted Indoor Localization Using Hybrid Wireless Data Fusion
Abouzar Ghavami, Ali Abedi