Call for Chapters

Internet of Things (IoT) is an interconnection of several devices, networks, technologies and human resources to achieve a common goal. There is a variety of IoT based applications that are being used in different sectors and have succeeded in providing huge benefits to the users. Data Analytics (DA) is defined as a process which is used to examine big and small data sets with varying data properties to extract meaningful conclusions from these data sets. Data Analytics has a significant role to play in the growth and success of IoT applications and investments. The utilization of data analytics shall, therefore, be promoted in the area of IoT to gain improved revenues, competitive gain, and customer engagement. In recent years, Deep Learning approaches have emerged as powerful computational models and have shown significant success to deal with a massive amount of data in unsupervised settings. Deep learning is revolutionizing because it offers an effective way of learning representation and allows the system to learn features automatically from data without the need of explicitly designing them. With the emerging technologies on the Internet of Things, wearable devices, cloud computing and data analytics offer the potential of acquiring and processing tremendous amount of data from the physical world. Promising computing paradigms and advanced technologies (e.g., Smart home or city) relating to context awareness systems, activity recognition, distributed smart sensing, heterogeneous big data analytics, and deep learning, etc., have been increasingly developed and integrated into this IoT systems in order to make IoT a reality.

Topics of Interest

This edited book solicits contributions from the field of IoT Data analytics using deep learning. Each book chapter should cover the solutions with the state-of-the-art and novel approaches for the IoT problems and challenges in Deep learning perspectives.

Topics to be discussed in this edited book include but not are limited to:

• Introduction to Deep learning and IoT
• Recent trends and advances in DL based IoT
• Heterogeneous Big data analytics for edge/fog Computing
• Novel Network Structures and System Design for DL enabled IoT
• Deep Learning architecture for IoT security
• Deep Learning experiments, test-beds and prototyping systems for IoT security
• Context-Aware Deep Learning based approaches for IoT
• Data confidentiality, Trust and privacy in DL enabled IoT Applications
• Analysis of Network Dynamics in IoT (this belongs to only IoT)
• Authentication and access control for data usage in IoT (this belongs to only IoT)
• Data mining and statistical modelling for the secure IoT
• User Interface design and implementation for DL enabled IoT
• User Interface design and evaluation for DL enabled IoT
• IoT/big data visualization techniques and application of DL
• Activity recognition in a smart home/city using deep learning