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The evolution of next-generation wireless networks presents new opportunities and challenges for secure and privacy-aware AI-enabled edge computing. These networks introduce ultra-low latency, high data rates, and massive connectivity, enabling various innovative edge computing applications. However, ensuring robust security and protecting user privacy in this dynamic and heterogeneous environment requires novel approaches.

The ultra-low latency capabilities of next-generation networks empower time-sensitive applications like autonomous vehicles, remote surgeries, and immersive augmented reality experiences. High data rates enable seamless transmission of large volumes of data, facilitating real-time analytics, video streaming, and resource-intensive applications. Furthermore, the massive connectivity of these networks allows for seamless integration of a vast array of devices, including Internet of Things (IoT) devices, into the network fabric. However, the dynamic and heterogeneous nature of next-generation wireless networks also introduces new security and privacy challenges. With an increased number of connected devices and diverse applications, the attack surface expands, making security breaches and unauthorized access more likely. Additionally, the immense volume of data generated and processed in edge computing environments raises concerns about user privacy and data protection.

This research aims to investigate secure and privacy-aware AI-enabled edge computing in next-generation wireless networks, exploring techniques to mitigate threats, preserve privacy, and enhance the trustworthiness of edge-based AI applications. Topics include, but are not limited to the following:

• Explainable and trustworthy AI in edge computing
• Efficient training and fast inference in the edge-cloud continuum
• Privacy-preserving data aggregation in AI-enabled edge computing
• Secure authentication and access control in edge computing
• Threat detection and mitigation in AI-enabled edge computing
• Secure and privacy-preserving machine learning algorithms for edge computing
• Lightweight cryptography for secure communication in edge computing
• Adversarial defense in privacy protection for AI-driven edge computing
• Federated learning with privacy and security in edge computing
• Secure and private data sharing in AI-driven edge computing
• Fairness and bias mitigation in federated learning
• AI-driven secure and private communication in edge computing
• AI-driven intrusion detection and prevention in edge computing

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE OJCOMS guidelines. Authors should submit a manuscript trough Manuscript Central.

Important Dates

anuscript Submission Deadline: 31 July 2024
Publication Date: Fourth Quarter 2024

Lead Guest Editor

Shaohua Wan
University of Electronic Science and Technology of China, China

Guest Editors

Zhipeng Cai
Georgia State University, USA

Quanyan Zhu
New York University, USA

Sotirios K. Goudos
Aristotle University of Thessaloniki, Greece

Athanasios Vasilakos
University of Agder, Norway

Carla Fabiana Chiasserini
Politecnico di Torino, Italy