Topics

At present, there is a growing number of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems. These solutions facilitate the creation of new products and services in many different fields. Sensor networks (SNs) are undergoing great expansion and development and the combination of both AI and SNs are now realities that are going to change our lives. The integration of these two technologies benefits other areas such as Industry 4.0, Internet of Things, Demotic Systems, etc. Furthermore, sensor networks (SNs) are widely used to collect environmental parameters in homes, buildings, vehicles, etc., where they are used as a source of information that aids the decision-making process and, in particular it allows systems to learn and to monitor activity. New AI and ML real time or execution time algorithms are needed, as well as different strategies to embed these algorithms in sensors. New clustering and classification techniques, reinforcement learning methods, or data quality approaches are required, as well as distributed AI algorithms.

This book calls for innovative work that explores new frontiers and challenges in the field of applying AI algorithms to SNs. As mentioned previously, this work will include new machine learning models, distributed AI proposals, hybrid AI systems, etc., as well as case studies or reviews of the state-of-the-art.

List of Topics

Part I- AI techniques for IoT-sensor networks

• Artificial Intelligence models for IoT-sensor Networks.
• Machine Learning models for IoT-sensor Networks.
•  Fuzzy Systems proposals for IoT-sensor Networks.
• Deep and reinforcement learning for IoT-sensor Networks.
• Intelligence image processing algorithms for IoT-sensor Networks.
• Expert Systems for IoT-sensor Networks.

Part II- Advanced Big Data processing technologies for IoT-sensor networks

• Evolutionary algorithms for big data mining in IoT-sensor networks
• Cloud computing-based evolutionary algorithms for IoT-sensor networks
• Artificial intelligence-based heuristic approach for reducing energy consumption in IoT-sensor networks
• Modelling and optimizing features selection in big data system-based social IoT-sensor networks
• Hybrid modelling using IoT and cloud computing to manage big data in an intelligent service system

Part III- AI techniques for Cloud based IoT-sensor networks

• AI for smart data storage in cloud-based Internet of Things
• Cognitive aspects of AI in cloud-based Internet of Things
• Intelligent interfaces for cloud-based Internet of Things
• Intelligent algorithms for cloud-based Internet of Things
• Deep learning for cloud-based Internet of Things
• Knowledge representation in cloud-based Internet of Things

Submission Guidelines

• Prospective authors should submit their manuscripts electronically through Easychair submission system or email through this email: [ Mohamed_elhoseny@mans.edu.eg , Mohamed.elhoseny@unt.edu]
• Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance.
• Authors should make sure that their submission has 15% or lower similarity as per iThenticate or Turnitin. Contributors may also be requested to serve as reviewers for this project.

Editors

Dr. Mohamed Elhoseny: Faculty of Computers and Information, Mansoura University, Egypt, Email: Mohamed_elhoseny@mans.edu.eg

Dr. K. Shankar: Department of Computer Applications, Alagappa University, Karaikudi, India. Email: shankarcrypto@gmail.com

Dr. Mohamed Abdel-Basset: Faculty of computers and informatics, Zagazig University, Egypt. Email: analyst_mohamed@zu.edu.eg 

Publications

AITIoT'19 will be published by series on Distributed Sensing and Intelligent Systems (Scopus Indexed) by Chapman & Hall/CRC Press.

Contact

All questions about submissions should be emailed to mohamed_elhoseny@mans.edu.eg or shankarcrypto@gmail.com  or analyst_mohamed@zu.edu.eg