Special Issue: AI-Powered Security- Privacy for Fog Computing and Industrial Internet of Things

Theme and Scope

The rapid proliferation of IoT devices enables a tremendous amount of data to be generated at a faster pace. As the increasing number of connected devices comes into use across the Industrial Internet of Things (IIoT) environment, traditional cloud computing architectures remain no longer a perfect solution. Due to this data explosion, cloud computing infrastructures face numerous difficulties in terms of data storage, computation, and management. The major limitation here is that it is often time-consuming for the cloud servers to act on the data as it is located far away from the IoT endpoints. Further, the centralised systems will not cope well with the scale and security requirements of the millions of IoT devices. They deal with numerous security constraints such as availability over intermittent network connections,  authentication, access control, secure storage, real-time analysis of the near-constant data stream. In the view of the traditional approaches, the data is moved from the edge to the central server for processing, which results in increased latency and reduced bandwidth measures across the network. To the core, the transfer of massive amounts of data from the IIoT devices at the edge to the central systems highly increases the probability of security breaches, resulting in lost and compromised information.

Astonishingly, the rapid growth of artificial intelligence (AI) driven IoT products calls for the transition from cloud-centric to fog computing platforms. Though still in its infancy, fog computing has been successfully rolled out among various IIoT applications. Fog computing provides the distributed computing architecture. The computing power and business logic are distributed most efficiently and logically between the data produced from IIoT devices and the cloud. Though the advances in technology have made the IoT devices in the industrial sector smarter, at the downside, it lacks security measures, and it needs the highest level of protection. The effective implementation of AI plays a massive role in enhancing traditional cybersecurity measures, especially among IIoT devices. It helps improve threat intelligence, prediction, and protection. Further, AI can learn from the security analysis and enhance its performance over time, leading to better decision making in a timely manner. In general, it does not wait for the cyberthreat to occur, but it efficiently predicts the possibility of the threat through historical data. In addition, it requires no human interventions and works appropriately in critical situations. Most importantly, it offers numerous benefits for IIoT when converged with decentralised fog computing architectures.

This special issue aims to analyse the challenges, risks, opportunities, and benefits of implementing AI for a secure fog assisted IIoT environment in detail. We welcome researchers and practitioners working in this stream to present their novel and innovative solutions.

We solicit contributions from the following background:

  • Advances in AI for security and privacy in fog enabled security and privacy in IIoT
  • AI enabled secure architectures for fog assisted IIoT
  • Effective ways of enhancing the security of the IIoT application with fog computing and AI
  • State-of-the-art AI technology for enhancing security measures across IIoT platforms
  • AI assisted threat intelligence and intrusion detection systems for fog assisted IIoT
  • Privacy preserving distributed machine learning models for IIoT
  • AI for analysing human behaviours across IIoT networks to prevent vulnerable threats
  • Advances in deep learning for securing fog assisted IIoT systems
  • AI assisted security optimisation algorithms for IIoT
  • Cost efficient and reliable AI assisted intrusion detection system for IIoT applications
  • Scalable and trustworthy AI based models for enhancing security and privacy measures across fog assisted IIoT applications


Instructions for Manuscripts

Each paper, written in English, the maximum words number in each paper should be below 8,000 words, including references and illustrations. More information can be found at https://jit.ndhu.edu.tw/. Before submission authors should carefully read over the journal’s Author Guidelines, which are located at https://jit.ndhu.edu.tw/about/submissions#authorGuidelines.

Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at https://mc04.manuscriptcentral.com/internet-tech. When submitting papers, authors should specify that the manuscript is for Special Issue on “AI-Powered Security- Privacy for Fog Computing and Industrial Internet of Things”. The manuscript template can be found at https://jit.ndhu.edu.tw/about/submissions#authorGuideline.


Important Dates

Manuscript Submissions Due: 30 April, 2022

Author First Notification: 30 August, 2022

Revisions Due: 30 October, 2022

Final Notification: 30 November, 2022

Tentative Publication: the 4th quarter of 2023

 

Guest Editors:

Dr. Ankit Chaudhary [Leading Guest Editor]

Department of Computer Science, The University of Missouri, USA

E-mail: ankitchaudhary@ieee.org

 

Dr. Anand Bhojan

School of Computing, National University of Singapore, Singapore

E-mail: banand@comp.nus.edu.sg

 

Dr. Ing. Jagdish Lal Raheja

Automation & Control System Group, CSIR-Central Electronics Engineering Research Institute, Rajasthan

E-mail: jagdish@ceeri.ernet.in