Theme and Scope Artificial Intelligence (AI) technologies are profoundly transforming the landscape of modern networking, digital infrastructure, and industrial ecosystems. The convergence of AI, Internet of Things (IoT), Edge Computing, Large Language Models (LLMs) for Intelligent Services, AI-Driven Digital Twin and Metaverse Applications, and Big Data is accelerating the development of intelligent, decentralized, and autonomous systems. These advancements are expected to play a critical role in shaping the next generation of smart applications in the post-pandemic digital era. The following outlines several key prospects and research directions of AI-driven technologies for science, engineering, and industry: 1. Advanced Data Intelligence and Predictive Analytics AI technologies have significantly enhanced the ability to extract meaningful insights from large-scale, heterogeneous data sources. By integrating Big Data analytics, machine learning, and AI-driven network intelligence, organizations can achieve more accurate forecasting and real-time decision-making. These capabilities are especially critical in complex networked environments, where predictive models can optimize traffic flow, detect anomalies, and improve system resilience. Furthermore, AI-powered analytics can support emerging applications such as Digital Twins, enabling real-time simulation and predictive maintenance across cyber-physical systems. 2. Autonomous Systems and AI Agents in Intelligent Networks The emergence of AI Agents and Autonomous Systems is redefining how networks operate and adapt. Intelligent agents embedded within network infrastructures can autonomously monitor conditions, optimize resource allocation, and respond to dynamic changes without human intervention. In combination with Edge AI and IoT, these systems enable distributed intelligence, reducing latency and improving responsiveness in real-time applications such as smart cities, autonomous vehicles, and industrial automation. This paradigm shift is paving the way for fully self-organizing and self-healing networks. 3. Intelligent Automation and Edge-Cloud Collaboration AI-driven automation is revolutionizing traditional operational workflows across industries. The integration of Edge Computing with cloud-based AI enables efficient data processing closer to the source, minimizing latency and bandwidth consumption. This architecture supports scalable and real-time intelligent services, including smart manufacturing, healthcare monitoring, and intelligent transportation systems. Additionally, Blockchain technologies can be incorporated to ensure data integrity, trust, and decentralized coordination among distributed network entities. 4. Human-AI Interaction and Personalized Intelligent Services The advancement of Human-AI Interaction (HAI) is enabling more intuitive and adaptive systems that can collaborate effectively with users. AI technologies can analyze user behaviors and preferences to deliver personalized services, improving user experience and engagement. Applications range from intelligent recommendation systems to adaptive learning environments and context-aware services. The integration of Explainable AI (XAI) further enhances transparency and trust, ensuring that AI-driven decisions are interpretable and accountable. 5. Intelligent Decision Systems and Multi-Criteria Optimization With the increasing complexity of networked systems, Intelligent Decision Systems play a crucial role in supporting strategic and operational decisions. AI-enhanced Multi-Criteria Decision Making (MCDM) methods, including ANP, TOPSIS, and VIKOR, provide robust frameworks for evaluating multiple conflicting criteria in dynamic environments. These approaches are widely applicable in network optimization, resource allocation, risk assessment, and policy design, enabling more informed and adaptive decision-making processes. 6. Security, Privacy, and Trust in AI-Enabled Networks As digital systems become more interconnected, ensuring security and privacy is of paramount importance. AI technologies can enhance cybersecurity by enabling real-time threat detection, anomaly analysis, and proactive defense mechanisms. Moreover, the integration of Blockchain and privacy-preserving AI techniques (e.g., federated learning) can strengthen data security and trustworthiness in distributed environments. These technologies are essential for safeguarding critical infrastructures and maintaining user confidence in AI-driven systems. 7. Deep Learning and Machine Learning for AIoT and Industrial Applications The integration of Deep Learning (DL) and Machine Learning (ML) with Artificial Intelligence of Things (AIoT) is driving transformative advancements in industrial and cyber-physical systems. By embedding intelligent learning capabilities into interconnected devices and sensors, AIoT systems can autonomously perceive, analyze, and respond to dynamic operational environments. In industrial applications, DL and ML are widely adopted for predictive maintenance, fault diagnosis, and process optimization. Deep neural networks can effectively analyze high-dimensional sensor data to detect anomalies early, reducing downtime and maintenance costs, while machine learning techniques enhance production scheduling, resource allocation, and supply chain efficiency. The convergence of Edge AI with DL/ML enables real-time analytics at the device level, minimizing latency and reducing dependence on centralized cloud infrastructures. This is particularly critical for smart manufacturing, robotics, and autonomous systems requiring rapid decision-making. Furthermore, Industrial AIoT (IIoT) is evolving toward intelligent and collaborative ecosystems aligned with Industry 4.0 and Industry 5.0, emphasizing human-centric design, sustainability, and system resilience. This special issue will systematically overview and present the latest research in network technology, intelligent computing, IoT, and computer engineering, with a particular emphasis on Artificial Intelligence (AI), Edge Computing, Blockchain, Big Data, and AIoT. It aims to highlight novel methodologies, system architectures, and innovative applications across interdisciplinary domains. Submissions should present original ideas, theoretical advancements, and practical implementations in, but not limited to, the following topics:
Submission Guidelines
Important Dates
Guest Editors
|
Submission
ISSN 1607-9264
e-ISSN 2079-4029
JCR 2024 Impact Factor: 1.2
CiteScore 2024: 3.1
SJR 2024: 0.312
SNIP 2024: 0.636
- Open Access
- Peer Review
- Reasonable APC
User
Subscription
Login to verify subscription
Information