Quantum AI-Enhanced IoT–Fog Communication: A Survey From Cybersecurity and Data Privacy Perspective

Abstract

The need for data privacy in the next-generation communication networks encompassing the Internet of Things (IoT) and Fog infrastructure has become very significant. This enforces the need for Quantum Artificial Intelligence (AI) approaches to safeguard them. The evolving new means of threats, which are complex and challenging to predict, make conventional security solutions difficult to address and mitigate. To counteract them proactively and efficiently, most organizations have started using AI solutions, which analyze and predict patterns of threats. However, most recent threats demand more robust solutions integrated into the network infrastructure. To sort out most of the demands of IoT-Fog communication services, we present a comprehensive review of Quantum AI to provide a secure and robust framework. Specifically, we provide a taxonomy to summarize the studies on Quantum AI over the IoT-Fog infrastructure, intended to provide predictive maintenance, mitigating threats, and robust defense strategies. Furthermore, we propose the integration of Neurosymbolic AI, which combines the pattern recognition power of neural networks with the reasoning capabilities of symbolic systems, thereby enabling context-aware threat detection and explainable decision-making in critical infrastructure security. In addition, we also emphasize network protocol security and communication privacy issues, particularly in industrial and cyber-physical system networks. Finally, we discuss prominent research challenges and open-ended future research directions for Quantum AI in next-generation wireless networks.