Special Issue "Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing"

Submission Deadline: 31 July 2021 (closed)
Guest Editors
Dr. Nawab Muhammad Faseeh Qureshi, Sungkyunkwan University, South Korea.
Prof. Guangjie Han, Hohai University, China.
Dr. Uttam Ghosh, Vanderbilt University, USA.
Dr. Varun G Menon, SCMS School of Engineering and Technology, India.


Pervasive computing refers to facilitating systems through devices, data, and communication channels for solving the issues anytime and everywhere. This technique has evolved with time and involves the Internet of Things (IoT), Big data management, and Machine learning that efficiently presents solutions in the distributed computing environment. Due to large-scale data processing involvement nowadays, these solutions rely on context-aware data processing and require robust machine learning algorithms to resolve the issues within an IoT device and big data repository.

An IoT device's architecture consists of three sections: cloud, edge, and device layers. The cloud layer manages data arrival and departure with the facility to compute and apply machine learning methods. The edge layer indicates the functional connectivity facilities to the cloud and big data repository so that these devices could store the sensory data anytime and everywhere. And the device layer includes incoming and outgoing connection channels that carry the data payloads to other IoT devices and big data repository. Thus, an IoT device does not facilitate users to solve any technical issues through data observation if found within that device. To reshape the IoT device to an intelligent IoT device, we require algorithms and techniques to analyze the cloud, edge, and device layers' problems.

This special issue seeks conceptual, empirical, or technological papers that will offer new insights into the following topics, but is not limited to them:


• Context-aware data algorithm solutions for IoT devices

• Big data management techniques for rectifying IoT devices issues

• Machine learning algorithms for addressing IoT devices problems

• Programmable Pervasive approaches for delivering IoT device solutions

• Predictive, prescriptive, descriptive analytics for IoT device issues

• Deep learning techniques for identifying micro issues in IoT devices

• Environmental issues for figuring out IoT device issues

• Embedded solutions for IoT device problems

• Security issues in the IoT devices

• Network processing problems in the IoT devices

• Multihoming data exchange issues in the IoT devices

• Reprogrammable approaches for solving IoT devices issues

• Hardware Abstraction Layer logs analytics for solving IoT device problems

Intelligent Data Management, Big data, Machine Learning, Internet of Things, Pervasive Computing, Deep Learning, IoT devices issues, Multihoming networking

Published Papers

  • Denoising Medical Images Using Deep Learning in IoT Environment
  • Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique… More
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  • A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence
  • Abstract Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated… More
  •   Views:692       Downloads:574        Download PDF

  • Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning
  • Abstract With every passing day, the demand for data traffic is increasing, and this urges the research community not only to look for an alternating spectrum for communication but also urges radio frequency planners to use the existing spectrum efficiently. Cell sizes are shrinking with every upcoming communication generation, which makes base station placement planning even more complex and cumbersome. In order to make the next-generation cost-effective, it is important to design a network in such a way that it utilizes the minimum number of base stations while ensuring seamless coverage and quality of service. This paper aims at the development… More
  •   Views:1460       Downloads:795       Cited by:1        Download PDF