The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

Software-Defined networking (SDN) is a networking paradigm to enable dynamic, flexible, and programmatically efficient configuration of networks to revolutionize network control and management via separation of the control plane and data plane. The SDN technology has evolved in response to the demands from large data centers toward all types of networks, from IoT, enterprise, to ISP networks. On the one hand, SDN has provided solutions for high-demand resources, managing unpredictable data traffic patterns, and rapid network reconfiguration. It is further used to enhance network virtualization and security. On the other hand, SDN is still subject to many traditional network security threats. It also introduces new security vulnerabilities, primarily due to its logically centralized control plane infrastructure and functions. In this paper, we conduct a comprehensive survey on the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put forward to address the challenges in securing SDN-based communications, with efforts taken up to address them. We further categorize the appropriate solutions for specific threats at each layer of SDN infrastructures. Lastly, security implications and future research trends are highlighted to provide insights for future research.

The digital twin has recently emerged as a virtual representation, that enables a real-time digital counterpart of a process or a physical object. Further, as the investments in Industry 5.0 are growing rapidly, their primary focus is to enhance the interactions between cyber-physical systems (CPS) and humans, for which outstanding contribution is expected through the metaverse. It enables humans to immerse into a high-dimensional 3-D virtual world, tackles the interactions among the CPS, and explore their status, which is found to be promising through the digital clones of CPS. This work presents the service-oriented digital twin architecture in conjunction with metaverse-enabled platforms with recommendations for ambitious interactions with the CPS for Industry 5.0 scenarios and beyond. They account for revolutionary changes in modern industries, supported through the Internet of Everything (IoE), VR/AR gadgets, and extended reality (XR) as prominent technology enablers.

Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework’s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.

Smart city is a transformative and progressive vision that aims to revolutionize infrastructure systems and public services in an urban area with modern information technologies. Its ultimate goal is to greatly improve the livability Quality of Service (QoS) of its citizens and to optimize the utilization of its assets and natural resources sustainably. One of the key technical attributes in smart cities is to deploy a large number of sensors to collect data to enable real-time and intelligent decisions for various city functions and citizen needs. Many of the data have strict security requirements as they are either private to citizens or sensitive to critical infrastructures. As a result, how to securely and efficiently deliver and process the dramatically increasing volume of data becomes one of the grand challenges in materializing the smart city vision. In recent years, Software-Defined Networking (SDN) has emerged as a leading communication infrastructure candidate for smart cities. While many efforts have existed to research, prototype, and even deploy SDN on a small scale for some smart city applications, there is still a lack of cohesive understanding about SDN’s impact on the secure communication need of smart cities. In this paper, we conduct a comprehensive survey of the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put on the security threats and challenges in accordance with SDN plane-based architectures for various smart city-enabled applications. We further systematically categorize the state-of-art solutions and proposals to apply SDN to support typical smart city applications, such as transportation, health, and energy applications. Lastly, we cast a holistic view of future research trends.