Special Issue (2026-03-01): Cognitive IoT Architectures for Sustainable Smart Home Energy Automation

Background

In the era of rapid urbanization and climate change, energy consumption in residential environments has become a major concern for sustainability and environmental impact.

Traditional home energy systems often operate without real-time awareness of usage patterns, leading to inefficiencies, increased costs, and unnecessary carbon emissions. To address these challenges, Cognitive Internet of Things (CIoT) architectures have emerged as a next-generation solution for enabling sustainable and intelligent energy automation in smart homes. Cognitive IoT goes beyond conventional IoT by embedding machine learning, context-awareness, and decision-making capabilities into the network of connected devices.

Unlike standard IoT systems that simply collect and transmit data, CIoT systems can perceive, learn, reason, and adapt to user behaviors and environmental conditions. This cognitive functionality allows for the dynamic optimization of energy usage, personalized to each household’s habits, preferences, and occupancy patterns.

At the core of a CIoT-based smart home energy system is a multi-layer architecture that typically includes edge devices (smart meters, thermostats, sensors), a communication layer (Wi-Fi, Zigbee, 5G), an edge or fog computing layer for local processing, and a cloud-based cognitive engine. These components work together to monitor energy consumption in real-time, predict future usage, and autonomously adjust device operations to reduce wastage.

For instance, a cognitive system can learn that residents leave for work at 8 a.m. daily and automatically power down non-essential appliances, or preheat water just before arrival based on learned behavior patterns and weather conditions. In addition, predictive analytics and reinforcement learning play a pivotal role in enhancing CIoT performance. By analyzing historical data, the system can forecast energy demand peaks and suggest optimal times for running energy-intensive tasks, such as laundry or electric vehicle charging. Integration with renewable energy sources, such as solar panels, further improves sustainability. The CIoT system can store excess energy during low-demand periods or feed it back into the grid, thus supporting distributed energy resources (DERs) and smart grid interaction. However, the deployment of cognitive IoT in home energy automation faces challenges, including data privacy, cybersecurity, interoperability, and cost. Ensuring secure communication between devices and protecting user data from breaches is critical. Moreover, standardization is essential to allow devices from different manufacturers to work seamlessly in a unified cognitive framework.

Aim

The primary objective of this SI is to collect the papers to advance the field of cognitive IoT in the context of smart home energy automation, and to position this journal as a distinctive outlet for cutting-edge research in this emerging domain. We encourage submissions from all global regions to ensure a comprehensive understanding of how cognitive IoT is being applied and adapted across different socio-economic and climatic contexts. In particular, we seek papers that offer original perspectives on how energy usage data is interpreted, acted upon, and integrated into policy, design, or user behavior from practical implementations to conceptual and analytical frameworks.

Scope

• Neuro-Symbolic Systems for Adaptive Energy Automation in Residential Environments
• ContextAware Computing for Personalized and Sustainable Home Energy Control
• Fog Computing Architectures for Low-Latency Cognitive Energy Management in Smart Homes
• Hybrid Fuzzy Logic and Neural Network Models for Intelligent Home Energy Decision Systems
• Voice-Activated Assistants Integrated with Cognitive IoT for Smart Energy Command Systems
• Ambient Intelligence for Eco-Adaptive Energy Automation in Future Homes
• Computer Vision-Based Occupancy Detection for Cognitive Smart Home Energy Management
• Cyber-Physical Systems for Intelligent and Sustainable Energy Distribution in Residences
• Ontology-Driven Reasoning Systems for Semantic Smart Home Energy Control
• Multimodal Sensor Fusion for Energy-Aware Behavior Modeling in Residential IoT Environments
• Interoperable Middleware Platforms for Cross-Device Cognitive Home Energy Automation
• Privacy-Preserving Federated Learning for Personalized Smart Energy Optimization
• Sustainable Home Energy Sharing Models via AI-Orchestrated Microgrid Systems

Important dates

• Paper Submission Deadline: 09.20.2026
• Papers are processed on a rolling basis as they are received.

Lead Associate Editor for the Special Issue:

Prof. Shang-Chin Lin, Feng Chia University (FCU), Taiwan.

Details of the Our Guest Editor Team:  

Lead Guest Editor

Professor. J.N.Swaminathan

Department of AIDS, JNN Institute of Engineering, Tamil Nadu, India

Official Email: [email protected]

Google Scholar: https://scholar.google.com/citations?user=VgcsaLIAAAAJ&hl=en

Expert Domains: Wireless Communication, Internet of Things (IoT), Machine Learning, Biomedical Engineering, Data Analysis, Security Design, Optimization, Energy Management.

Co-Guest Editors:

Dr. Oladapo Tolulope Ibitoye

Afe Babalola University, Nigeria.

Official Email: [email protected]

Google Scholar: https://scholar.google.com/citations?user=uCM8vkMAAAAJ&hl=en

Expert Domains: Internet of Things, Machine Learning, Computational Intelligence

Dr. Muhammad Nawaz Khan  

Department of Smart Security, Gachon University, South Korea.

Official Email: [email protected]

Google Scholar: https://scholar.google.com/citations?user=ObpAku8AAAAJ&hl=en

Expert Domains: Computer networks, IoT Information Security, Reinforcement Learning