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Publication Date


Manuscript Submission Deadline

Special Issue

Call for Papers

The rapid development of real-time Industrial Internet-of-Things (IIoT) applications including green infrastructure, smart grids, smart city, intelligent transport networks, etc. enables green communication between tens of billions of end devices such as wearable devices and sensors. As a result, a tremendous amount of data is generated from massively distributed sources, which require computational intelligence techniques to fulfill high computing and communication demand that frequently exceeds the energy consumption. Many emerging IIoT applications including remote surgery, machine monitoring and control, fault detection, and healthcare generate delay-sensitive tasks, which require timely processing with minimum delay. Besides, according to the energy consumption formulation, the required energy consumption for processing the real-time tasks on the remote computing devices should be the accumulation of data transmission time, transmission power, and processing capacity. Thereby, the energy emission rate can be controlled by balancing the trade-off between the transmission power and transmission time. IIoT covers a broad domain of real-time IIoT applications and refers to the combination of IoT technologies and computational intelligence techniques for processing real-time data with minimum delay. In addition, energy-efficient communication and computation of the real-time IIoT applications target to increase efficiency, automation, and productivity.

Recent advances in artificial intelligence (AI)-enabled techniques including advanced machine learning (ML) and deep learning (DL), bring many key research directions to analyze the computational intelligence framework by monitoring the real-time information and sensed data. Despite various advantages of the integration of computational intelligence techniques for various IIoT applications, the appropriate application of the AI model poses several challenges including data volume and quality, integration, and accuracy of the inferences drawn from the collected data. Besides that, advanced computational intelligence techniques such as distributed and federated learning are selected to train the local edge/fog devices locally and produce a global model under the coordination of a central edge/fog/cloud server. In recent times, advanced computational intelligence techniques for IIoT have attracted great interest from academia and industry.  

The Special Issue aims to gather the recent advances and novel contributions from academic researchers and industry practitioners in the area of computational intelligence techniques for the Next-generation IIoT applications to fully leverage the potential capabilities and opportunities brought by this area. In this issue, we mainly devise five main technical directions for research to provide contributions for developing sustainable services in IIoT, namely computational intelligence techniques; fog/edge computing; green communication and computing; smart infrastructure deployment; and ML/DL models. The topics of interest for this Special Issue include, but are not limited to:

  • Ultra-reliable and low latency communication protocol for intelligent IIoT applications
  • Green communication for collaborative computational intelligent IIoT applications
  • Green fog/edge platform and caching techniques for AI-enabled IIoT application
  • Computational Intelligence techniques for resource management of IIoT applications
  • Energy-efficient D2D/M2M communications for AI-enabled IIoT applications
  • Novel collaborative frameworks/algorithms/protocol for intelligent IIoT applications
  • Collaborative computational intelligence techniques for intelligent transportation systems
  • Advanced ML/DL models for handling IIoT applications & predictive analysis of big data
  • Advanced AI models for real/industry applications and systems for IIoT
  • Smart threat models and risk management for intelligent IIoT applications
  • Energy-efficient harvesting techniques for green communications and networking
  • Federated/distributed learning framework for IIoT applications at edge/fog networks
  • Blockchain-based IIoT framework for domain-specific IIoT applications
  • Computational intelligence techniques for real-time IIoT application
  • Experimental prototyping and testbeds for Intelligent IIoT applications
  • Computational intelligence scalable hybrid systems for IIoT applications
  • Computational intelligence techniques for decision-making schemes of IIoT applications
  • Advanced AI model for future generation IIoT applications
  • Computational intelligence techniques for smart home/city/healthcare/grid

Submission Guidelines

Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Network Science and Engineering guidelines. Note that the page limit is the same as that of regular papers. Please submit your papers through the online system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail directly to the Guest Editors.

Important Dates

Paper Submission Due: 30 October 2021 31 December 2021 (Extended Deadline)
First Notification: 31 January 2022
Revisions Due: 31 March 2022
Final Decision: 30 June 2022
Issue of Publication: 2022

Guest Editors

Dr. Mainak Adhikari (Lead)
University of Tartu, Estonia

Dr. Varun G Menon
SCMS School of Engineering and Technology, India

Prof. Haris Gačanin
RWTH Aachen University, Germany

Prof. Octavia A. Dobre
Memorial University, St. John’s, NL, Canada

Prof. Danda. B Rawat
Howard University, USA

Dr. Xingwang Li
Henan Polytechnic University, China