Innovations in AI and Distributed IoT-Edge-Cloud Computing for Next-Generation Networks

Dear Colleagues,

The rapid expansion of connected devices and the maturation of machine learning techniques have ushered in a new era of distributed computing, in which intelligence can be embedded from tiny sensors at the network edge all the way to massive data centers in the cloud. As IoT ecosystems become more pervasive, from wearable health monitors and smart home appliances to industrial control systems and urban infrastructure, there is an urgent need for computing architectures that can process data locally when latency, privacy, or connectivity constraints demand it, while still leveraging centralized resources for deep analytics and long-term model training.

This Special Issue is dedicated to advancing the state of the art in next-generation IoT–edge–cloud networks. Our goal is to bring together cutting-edge research that not only demonstrates the potential of AI methods for inference, learning, and decision making in resource-constrained environments, but also showcases novel systems engineering approaches to orchestration, virtualization, and workload management across heterogeneous hardware. We particularly encourage submissions that present end-to-end solutions, in which algorithmic innovation and systems design co-evolve, to achieve real-world performance, efficiency, and reliability targets.

Contributors should provide rigorous empirical evidence of their ideas. Testbed implementations, hardware-in-the-loop experiments, and large-scale emulations all serve to validate theoretical advances and system prototypes. Detailed analyses of trade-offs among latency, throughput, energy consumption, and model accuracy will offer valuable insights for both researchers and practitioners. Equally important are studies that identify and address challenges in deployment, such as intermittent connectivity and privacy considerations, and evolving workload patterns in dynamic environments.

By uniting expertise from machine learning, distributed systems, and networking, this issue seeks to define best practices and chart a roadmap for resilient, high-throughput, and intelligent edge-enabled infrastructures. Interdisciplinary collaborations, spanning those from algorithm developers, middleware designers, hardware architects, and domain specialists, are especially welcome, as they tend to yield robust, adaptable solutions that thrive beyond laboratory settings.

This issue anticipates contributions in areas such as lightweight model compression and on-device inference, federated and collaborative learning under network variability, software-defined orchestration for microservices and functions, hardware acceleration in constrained nodes, and context-aware offloading strategies. We also invite case studies that illustrate successful deployments in domains like smart cities, industrial automation, autonomous systems, immersive experiences, and environmental monitoring.

Ultimately, this Special Issue aspires to catalyze progress toward distributed computing networks that are not only intelligent but also self-optimizing, secure, and sustainable. By showcasing the latest breakthroughs in both AI and systems engineering, we aim to foster a community that can address the complex demands of future IoT–edge–cloud applications and drive the development of infrastructures capable of supporting next-generation services.

Topics of interest include, but are not limited to, the following:

1. Federated learning in decentralized edge environments;
2. Deep reinforcement learning for dynamic resource management;
3. Energy- or latency-aware orchestration for edge–cloud–IoT systems;
4. Real-time task offloading using drones as mobile edge nodes;
5. AI-driven resource allocation in heterogeneous infrastructures;
6. Multimodal data fusion pipelines in distributed systems;
7. Digital twins leveraging live IoT data and predictive AI models;
8. Predictive workload balancing and smart task distribution using microservices;
9. Self-organizing networks (SONs) for 5G/6G-integrated IoT systems;
10. Urban-scale applications using AI across distributed infrastructures.

Dr. Rasha S. Gargees
Prof. Dr. Ahmed Abdelgawad
Guest Editors

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• cloud/edge computing
• IoT
• distributed artificial intelligence
• resource management