Journal Article
Quantum AI-Enhanced IoT-Fog Communication: A Survey From Cybersecurity and Data Privacy Perspective
An IEEE Communications Surveys & Tutorials article on Quantum AI for cybersecurity and data privacy in next-generation IoT-Fog communication, 6G services, industrial systems, and cyber-physical infrastructure.
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.
Plain-Language Summary
This paper looks at how Quantum AI can strengthen privacy and security for connected devices and fog computing systems, especially where cyber-physical infrastructure needs fast and explainable threat detection.
Why This Paper Matters
IoT-Fog communication systems increasingly support healthcare, smart cities, industrial automation, transportation, energy systems, and 6G services. These environments process sensitive data close to the edge while facing evolving cyber threats. This survey explains how Quantum AI, post-quantum security, and neurosymbolic reasoning can contribute to more adaptive, explainable, and privacy-aware cybersecurity for distributed infrastructure.
Research Summary
This paper examines the security and privacy needs of next-generation IoT-Fog communication systems. These systems connect large numbers of devices, edge resources, and cyber-physical infrastructure, making them difficult to protect with conventional static security mechanisms.
The survey focuses on Quantum AI as a possible way to strengthen threat detection, predictive maintenance, robust defense, and privacy-preserving communication. It also discusses the role of neurosymbolic AI in making threat detection more context-aware and explainable.
The broader value of the paper is its synthesis of security challenges across IoT, fog computing, industrial networks, and cyber-physical systems. It frames Quantum AI not as an isolated technique, but as part of a future security stack for adaptive and explainable next-generation communication networks.
Quantum AI Security Framework for IoT-Fog Communication
Threat Detection
Uses Quantum AI and machine learning to identify anomalies, intrusions, and complex attack patterns across decentralized IoT-Fog networks.
Privacy Protection
Addresses secure data transmission, communication privacy, quantum key distribution, and post-quantum cryptographic protection for sensitive edge data.
Explainable Cyber Defense
Integrates neurosymbolic AI to make threat detection more context-aware, interpretable, and useful for cybersecurity operators.
Resilient Infrastructure
Focuses on industrial systems, cyber-physical networks, 6G communication, real-time monitoring, and robust defense strategies.
Key Contributions
- Surveys Quantum AI techniques for IoT-Fog cybersecurity and data privacy.
- Builds a taxonomy of studies across threat mitigation, predictive maintenance, and robust defense.
- Highlights neurosymbolic AI as a route toward context-aware and explainable security decisions.
- Identifies open challenges for Quantum AI in next-generation wireless and cyber-physical networks.
Modeling Approaches Reviewed
Quantum Machine Learning
Applies quantum-enhanced learning models such as quantum neural networks and quantum support vector machines for cybersecurity analytics.
Post-Quantum Cryptography
Prepares IoT-Fog systems for quantum-era threats by replacing vulnerable classical cryptography with quantum-resistant protocols.
Quantum Key Distribution
Supports secure communication channels that are resistant to traditional eavesdropping attacks.
Neurosymbolic AI
Combines neural pattern recognition with symbolic reasoning to improve explainability and context-aware threat detection.
Federated and Edge Intelligence
Supports distributed learning and local decision-making across resource-constrained fog nodes and IoT devices.
Research Gaps
Publication Details
- Type
- Journal Article
- Venue
- IEEE Communications Surveys & Tutorials
- Year
- 2026
- Volume
- 28
- Pages
- 3637-3665
Authors
Research Topics
Links and Access
Citation
@article{deMacedo2026quantum,
author={de Macêdo, Antônio Roberto L. and Jagatheesaperumal, Senthil Kumar and da Costa, Kelton Augusto Pontara and Acharya, Kamal and Song, Houbing and Guizani, Mohsen and de Albuquerque, Victor Hugo C.},
title={Quantum AI-Enhanced IoT-Fog Communication: A Survey From Cybersecurity and Data Privacy Perspective},
journal={IEEE Communications Surveys & Tutorials},
year={2026},
volume={28},
pages={3637--3665},
doi={10.1109/COMST.2025.3622378}
}