Trustworthy and Privacy-Aware Intelligence for Next-Generation IoT and Edge Networks

Dear Colleagues,

The rapid convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and distributed computing paradigms is reshaping how data are collected, processed, and trusted across edge and cloud infrastructures. While AI enables autonomous and adaptive decision-making, AI systems also introduce critical challenges in terms of privacy, explainability, and trust.

This Special Issue aims to explore how trustworthy and privacy-aware intelligence can be embedded into next-generation IoT and edge networks. We seek contributions that combine advances in AI for networked systems, privacy-preserving machine learning, and secure, scalable architectures for Cyber-Physical Systems (CPSs). This Special Issue will particularly focus on approaches that enhance the reliability, transparency, and ethical use of AI in distributed environments.

We welcome theoretical, methodological, and application-oriented papers addressing real-world deployments of AI in smart cities, industrial automation, environmental monitoring, and connected mobility. Particular attention will be given to contributions that address trade-offs between performance and privacy or that propose frameworks that ensure transparency, accountability, and resilience in AI-driven IoT ecosystems.

By bringing together perspectives from AI, network engineering, and privacy research, this Special Issue aims to advance the design of intelligent, secure, and sustainable infrastructures that will form the foundation of trustworthy and human-centered digital environments.

Topics of Interest

AI and Network Intelligence:

• Distributed, federated, and continual learning for IoT systems;
• Edge AI, TinyML, and on-device intelligence;
• Large Language Models and Generative AI for IoT and CPSs;
• Explainable and trustworthy AI in networked environments.

Privacy, Security, and Resilience:

• Privacy-preserving Machine Learning;
• Data anonymization, differential privacy, and secure multiparty computation;
• Blockchain and DLT for trust and accountability;
• Privacy and security in LLM-based IoT applications.

Network and System Design:

• Energy-aware and sustainable edge/cloud architectures;
• AI-enabled network management;
• Simulation and modeling of privacy-aware IoT ecosystems;
• Integration of AI, IoT, and Blockchain for secure edge networks.

Applications:

• Smart city, environmental, and urban sensing;
• Healthcare and assistive IoT systems;
• Industrial and process automation;
• Mobility and transportation networks.

Dr. Michele Mastroianni
Dr. Lelio Campanile
Guest Editors

Manuscript Submission Information

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