***** To join INSNA, visit *****



Intelligent technologies represent a remarkable transformation for smart cities. Such technologies have enhanced the quality and performance of urban and sub-urban services, including transport, energy, healthcare and traffic. With today’s advancements in Artificial Intelligence (AI), user-specific service availability and delivery has been made readily available at reduced costs. As smart cities continue to deploy such intelligent and learning technologies to improve the performance and diversity of smart city services, one of the main issues that prevails is efficient and reliable security and privacy towards both the infrastructure and users. The hype surrounding 5G and AI is already forcing companies to quickly upgrade their current solutions, tools, and technology to accommodate massive data volumes. However, the large-scale deployment of 5G, Cloud, Fog and Mobile Edge Computing (MEC) will bring new challenges; security and privacy are some examples.

AI and machine learning provide a mechanism to automate the creation of analytical models in order to enable algorithms to learn continuously. This is achieved with the aid of the ever-growing smart city data. Continuously evolving successful models and solutions reduces the need for human involvement and interaction. These evolved models can be used to produce reliable and repeatable decisions autonomously. Such models are now being used in intelligent and autonomously-run smart vehicles. The advent of such new learning solutions leads to more security, privacy and cryptographic challenges.      

This special issue intends to explore and address the important issues and challenges related to the security and privacy for intelligent and learning technologies. The objective is to explore the potentials of machine learning and big data analytics to provide advanced solutions for integration within the smart city architecture. We believe that machine learning and big data analytics will play a vital role in providing enhanced security for intelligent autonomous smart city solutions and enables organizations to make crucial changes to their security landscape. Topics of interest include, but are not limited to, the following: 

·        Security and privacy architectures for smart cities.

·        Machine learning and big data analytics architectures for smart city security and privacy.

·        Data confidentiality and privacy in smart cities.

·        Using fog and MEC architectures to provide secure and reliable communication.

·        Providing secure and reliable Next Generation Networks.

·        Block chain solutions for next generation security and privacy.

·        Machine learning based security detecting protocols.

·        Secure fog, edge and cloud solutions.

·        Intrusion detection and prevention using intelligent and learning techniques


Instructions for Manuscripts

Submitted articles must describe original research which have not been published or currently under review by other journals or conferences. Submissions must be directly sent via the Security and Privacy submission website at Select ‘Special Issue’ as the manuscript type and then, from the dropdown list, please select ‘Security and Privacy in Smart Communities’. Submissions must conform to the layout and format guidelines for the journal:


Important Dates

Manuscript Due: September 31, 2019

Decision Notification: November 31, 2019

Final Manuscript Due: December 30, 2019

Tentative Publication Date: 3rd Quarter of 2020


 Guest Editors

Dr. Ismaeel Al Ridhawi, University of Ottawa, Ottawa, ON, Canada. [log in to unmask]

Dr. Omar Alfandi, Zayed University, United Arab Emirates and Georg-August-University Goettingen, Germany. [log in to unmask]

Prof. Jalel Ben Othman, L2S lab CNRS Centrale Supelec, University Paris Sud and University of Paris 13, France, [log in to unmask]

Dr. Moayad Aloqaily, Gnowit Inc., Ottawa, ON, Canada. [log in to unmask]  

_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers ( To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.