Special Issue "Deep Learning and Parallel Computing for Intelligent and Efficient IoT"

Submission Deadline: 29 January 2021
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Guest Editors
Dr. Irfan Uddin, Kohat University of Science and Technology, Pakistan.
Dr. Jia-Bao Liu, Anhui Jianzhu University, China.
Dr. Furqan Aziz, University of Birmingham, UK.
Dr. Shamsul Huda, Deakin University, Australia.
Dr. Muhammad Asif Manzoor, University of Regina, Canada.


Artificial Intelligence (AI) is recently becoming very popular mainly because of advancements in Machine Learning (ML), more specifically in Deep Learning (DL) and Reinforcement Learning (RL). A wide range of applications are using these techniques. Internet of Things (IoT) is the future generation system. The complex, heterogeneous and distributed nature of IoT devices has inspired many researchers and practitioners to explore the usage of AI/ML/DL techniques to make intelligent IoT. Parallel computing techniques are used to make these devices more efficient and reliable. As a result of this massive adaption and growth, smart cities, smart grid, smart healthcare and smart industries are emerged.

A large number of distributed heterogeneous devices are interconnected in IoT and a huge amount of data is generated. This data is increasing in size and heterogeneity. The network of IoT devices is diverse and complex in nature. These devices contain limited computational power, memory and energy resources. Therefore, AI/ML/DL based devices are important to develop intelligent IoT systems and efficient management of resource and network. The objective is to improve the overall performance of IoT systems.

This special issue aims to bring together the academic and industrial researchers to explore the opportunities of DL and parallel computing for IOT, study its impact on the solution of the aforementioned challenges and propose viable solutions.

We solicit papers covering various topics of interest that include but not limited to the following topics:

• Architecture and technologies for intelligent IoT using Deep Learning and Parallel Computing

• Services for smart systems based on Deep Learning (smart building, smart cities, smart grids, smart transportation, smart healthcare)

• Big data mining and analytics for intelligent IOT based on Deep Learning

• Applications for intelligent IoT based on Deep Learning

• Transport protocols for intelligent IoT based on Deep Learning

• Data management for IoT based on Deep Learning

• Application for energy efficient IOT systems based on Deep Learning

Deep Learning, Parallel Computing, GPUs, IoT and Performance improvements.

Published Papers
  • Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks
  • Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More
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  • Smart Object Detection and Home Appliances Control System in Smart Cities
  • Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More
  •   Views:66       Downloads:47        Download PDF