Guest Editors
Dr. Thippa Reddy Gadekallu, Vellore Institute of Technology, India.
Dr. Mamoun Alazab, Charles Darwin University, Australia.
Dr. Praveen Kumar Reddy M, Vellore Institute of Technology, India.
Summary
A green IoT energy-aware network plays an important role in sensing technology. IoT growth influences many applications such as e-health care, smart cities, vehicle systems, and industrial engineering in today's era. Some of the compromise solutions occur during the design and development of IoT-based applications such as security, resources, routing, node deployment, etc. The rapid increase in sensor nodes results in increased power consumption. Therefore, the reduction of environmental impact in green media networks is a crucial challenge for academic and industrial researchers. One of the IoT-based applications' problems is improving power efficiency and network longevity by miniature size, limited battery life, and dynamic motion of the sensor nodes in industrial applications. Energy optimization and resource management in these networks remain a challenge, Therefore for efficient power management and necessary optimization for IoT applications, Artificial Intelligence (AI), Deep Learning (DL) and other Neural Network (NN) based approaches will come up as a solution for green communication.
In addition, recent literature covered machine learning; deep learning algorithms can provide energy efficiency solutions, predictions on batteries, network monitoring, etc. This special issue seeks submissions of high-quality and unpublished articles to address the technical problems and challenges of green communications networks. In particular, we seek submissions that efficiently integrate new AI, DL approaches, focusing on the assessment of IoT ecosystem performance across existing green communication solutions. Theoretical and experimental studies for such scenarios are encouraged.
We solicit papers covering various topics of interest that include, but are not limited to, the following.
• Resource optimization in IoT applications.
• Machine learning approaches for green IoT.
• Quality of service in smart green communication networks for the IoT ecosystem.
• Architectures and models for smart green communication networks for IoT.
• Green communication network designs and implementations for IoT ecosystem.
• Innovative green communications technologies and protocols suitably designed for the IoT.
• Smart energy harvesting/charging and power management techniques using ML techniques.
• Energy-efficient sensing techniques.
• Experimental results and test-beds for smart green computing systems for IoT network models.
Keywords
Internet of Things, Energy Efficiency, Green Internet of Things, Machine Learning, Deep Learning
Published Papers
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