Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

Smart and collaborative industrial IoT: A federated learning and data space approach

This article explores the human challenges on Cybersecurity Culture at the time of AI, considering diversity, internet international cybercrime organizations, the “naive behavior” of the user, and the lack of technical capacity, skills, tools, and collaboration of the authorities, in different countries. The technological revolution promoted by artificial intelligence (AI) brings with it advances and challenges, especially in the field of cybersecurity. Governments around the world are moving to regulate the ethical use of AI, as in the EU and Brazil’s draft laws. Cybersecurity can benefit from AI, helping to detect threats and automate tasks for proactive security measures. However, it also introduces risks such as deepfake exploitation and sophisticated social engineering schemes, which pose financial, physical, and psychological threats. In addition, ubiquitous computing, especially in smart cities with IoT devices, amplifies security challenges, with APIs playing a key role in facilitating communication between interconnected devices, but also presenting risks such as authentication breaches and incorrect security settings that can lead to systems becoming unavailable. Therefore, this article will explain how to develop a digital economy reliable and safe, considering the new AI applications and the necessity to apply governance best practices and rules of ethics, privacy, security by design to have sustainable innovation to continue developing the industry 5.0 for the future.

Artificial intelligence has affected and is changing our lives, penetrating into all fields of society, and the field of law is no exception. The development of artificial intelligence judicature such as smart court is a positive response of judicial informatization to artificial intelligence technology. Chinese judicature, which has experienced the development of electronization, networkization and digitization, has achieved significant transformation, integration and development in the era of artificial intelligence. At present, artificial intelligence has realized some applications in court system, procuratorate system and public security system, but these applications still remain at the superficial and uniformed level. With the improvement of artificial intelligence optical character recognition technology, natural language processing technology, intelligent speech recognition technology, element extraction technology and machine learning ability, higher-level artificial intelligence judicature urgently needs to be further promoted to run through the whole judicial process of the public security department, the Procuratorate department, the court department, and the Justice department. The data communication between the judicial systems with the data centers should adopt the optical one-way security isolation data automatic import system. The internal network between the judicial system adopts a two-way one-way gatekeeper to build a secure communication network. Based on the Secure network, interconnected and shared data storage, management and Protection platform, convenient transaction processing platform, intelligent legal analysis platform, efficient judicial execution platform, stable judicial supervision platform, extensive legal service platform should be established.

With the world moving towards the era of Artificial Intelligence (AI), Internet of Things (IoT) and Industrial Internet of Things (IIoT) there is a need for technologies that align with the customer expectation of making their plants more intelligent and communicative in a wireless manner. The currently trending concept of Industry 4.0 introduces what has been called the “smart factory,” in which cyber-physical systems monitor the physical processes of the factory and make decentralized decisions. The physical systems become Internet of Things, communicating and cooperating both with each other and with humans in real time via the wireless web. The paper introduces SMART Electric Motors, the future of Electric Motors. A SMART Motor can perform its own condition monitoring, 24×7, and predicts failures much in advance, which allows corrective actions to be taken to avoid premature equipment & process breakdowns in the industry. SMART motors coupled with various equipment also act as a pattern learning device to gather the, much needed, hidden, equipment productivity and efficiency related information. This information can be a very useful MIS data to improve the productivity, efficiency and Overall Equipment Effectiveness (OEE) for the industry.

With the rise of the AI (Artificial intelligence) era, as AI technology deeply reshapes the accounting industry, and traditional accounting functions and work models are undergoing revolutionary changes. This research explores the new connotations and components of professional competencies for accountants in the AI era from the perspective of dynamic optimization of competency models. It constructs a four-dimensional competency framework: core competency-professional ability-technical capability-strategic thinking. Using literature review, case studies, and structural equation modeling, the research examines the status and gaps in the competencies of accountants. Besides,the research proposes three targeted approaches to reshape the professional competencies of accountants: improving the education and training system, establishing a dynamic evaluation mechanism, and optimizing the career advancement system. This research aims to provide theoretical support for revising accounting talent development programs in universities, enhancing corporate accounting talent development mechanisms, and promoting the self-improvement of accounting professionals, thereby assisting accounting practitioners in achieving sustainable career development in the AI era.

Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Especially, explosive time-series data pose enormous challenges to the information mining and processing for modern industry. How to identify and detect the multidimensional industrial time-series anomaly is an important issue. However, most of the existing studies fail to handle with large amounts of unlabeled data, thus generating the undesirable results. In this article, we propose a novel integrated deep generative model, which is built by generative adversarial networks based on bidirectional long short-term memory and attention mechanism (AMBi-GAN). The structure for the generator and the discriminator is the bidirectional long short-term memory with attention mechanism, which can capture time-series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multidimensional time-series anomaly toward IIoT in the era of artificial intelligence.

Internet of Things (IoT) is a concept that proposes the inclusion of physical devices as a new form of communication, connecting them with various information systems. Nowadays, IoT cannot be reduced to smart homes as due to the recent technological advances, this concept has evolved from small to large-scale environments. There was also a need to adopt IoT in several business sectors, such as manufacturing, logistics or transportation in order to converge information technologies and technological operations. Due to this convergence, it was possible to arrive at a new IoT paradigm, called the Industrial Internet of Things (IIOT). However, in IIoT, it is also necessary to analyze and interact with a real system through a virtual production, which refers to the use of Augmented Reality (AR) as a method to achieve this interaction. AR is the overlay of digital content in the real world, and even play an important role in the life cycle of a product, from its design to its support, thus allowing greater flexibility. The adoption of interconnected systems and the use of IoT have motivated the use of Artificial Intelligence (AI) because much of the data coming from several sources is unstructured. Several AI algorithms have been used for decades aiming at “making sense” of unstructured data, and transforming it into relevant information. Therefore, converging IoT, AR, and AI makes systems become increasingly autonomous and problem solving in many scenarios.

The continued rapid growth of data, along with advances in Artificial Intelligence (AI) to extract knowledge from such data, is reshaping the computing ecosystem landscape. With AI becoming an essential part of almost every end-user application, our current computing platforms are facing several challenges. The data-intensive nature of current AI models requires minimizing data movement. Furthermore, interactive intelligent datacenter-scale services require scalable and real-time solutions to provide a compelling user experience. Finally, algorithmic innovations in AI demand a flexible and programmable computing platform that can keep up with this rapidly changing field. We believe that these trends and their accompanying challenges present tremendous opportunities for FPGAs. FPGAs are a natural substrate to provide a programmable, near-data, real-time, and scalable platform for AI analytics. FPGAs are already embedded in several places where data flows throughout the computing ecosystem (e.g., smart network/storage, near image/audio sensors). Intel FPGAs are System-in-Package (SiP), scalable with 2.5D chiplets. They are also scalable at datacenter-scale as reconfigurable cloud, enabling real-time AI services. Using overlays, FPGAs can be programmed through software without needing long-running RTL synthesis. With further innovations, and leveraging their existing strengths, FPGAs can leap forward to realize their true potentials in AI analytics. In this talk, we first discuss the current trends in AI and big data. We then present trends in FPGA and opportunities for FPGAs in the era of AI and big data. Finally, we highlight selected research efforts to seize some of these opportunities: (1) 2.5D SiP integration of FPGA and AI chiplets to improve the performance and efficiency of AI workloads, and (2) AI overlay for FPGA to facilitate software-level programmability and compilation-speed.

The rapid integration of industrial automation and control systems into large-scale Industrial Internet of Things (IIoT) infrastructures has substantially increased system complexity. The complexity arises from the deployment of heterogeneous devices that must coordinate in real-time to support adaptive decision-making. As a result, a robust situation awareness (SA) framework for IIoT has become a critical goal of IIoT. This review investigates the role of SA in the design of controllers in industrial applications, with the aim of advancing control strategies and facilitating integration with emerging IoT technologies. The survey focuses on SA, Model Predictive Control (MPC), and their integration, based on time series as a standard data structure to represent device performance. A key finding from the current literature is that SA is often treated separately from control theory, resulting in isolated applications and not integrated with large-scale IIoT systems. This survey establishes the features and paradigms of SA in IIoT. It provides a structured analysis of controller design methods in IIoT. State-space models (SSMs) can be used to model temporal dynamics in IIoT data. Finally, the integration of artificial intelligence (AI) with SSMs is explored to achieve SA based on multivariate time series data.

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.

In the era of artificial intelligence (AI), physical education (PE) teachers face unprecedented challenges in integrating advanced technologies into their teaching practices. This study aims to identify the specific challenges PE teachers encounter in the AI era and propose practical strategies to enhance the quality and relevance of school-based physical education. This study employed a multi-method approach, including literature review, mathematical statistics, questionnaire surveys, and expert interviews. The participants were 120 PE teachers from urban and rural schools across different regions. The study began with a literature review to establish a theoretical foundation. Questionnaires were used to gather teachers' perceptions of challenges related to AI integration, student engagement, and professional development. In-depth interviews with 25 PE experts provided deeper insights into their experiences and coping strategies. Data were analyzed using SPSS software. The results showed that 80% of PE teachers reported difficulties in integrating AI tools into their lessons, while 70% struggled with engaging students in technology-enhanced activities. Analysis of the questionnaire and interview data revealed three main challenges: (1) limited access to AI resources, (2) lack of training in AI applications, and (3) concerns about the impact of AI on traditional teaching methods. Teachers emphasized the need for continuous professional development and support to effectively utilize AI in teaching. The study concludes that PE teachers face significant challenges in adapting to the AI-driven educational environment, particularly in integrating AI tools and maintaining student engagement. These findings align with previous research on educational technology integration but highlight the unique concerns of PE teachers regarding the impact of AI on traditional teaching. This study proposes the following strategies: (1) increasing investment in AI tools and establishing resource-sharing platforms to improve access to AI resources; (2) providing continuous professional training in AI applications and integrating AI education into teacher development programs; (3) offering technical support for AI integration and encouraging teachers to use AI tools to optimize traditional teaching methods.

Based on the current situation, problems and coping measures of network information security in the new era of artificial intelligence and mobile Internet, this paper explores various of network security risks faced by network information, and proposes a new quantum computing reconstruction encryption system based on artificial intelligence, large model and multi-modal fusion application scenarios, as well as network protection technology architecture with the ability to defend against deep fake, which is generated by generative artificial intelligence, and gives the enhancing encryption public key generation algorithm using pseudo-random sequence and verifies the feasibility of this encryption algorithm through encryption and decryption test experiments of different communication information formats, and analyzes the security of the algorithm. The experimental results show that network communication information encryption algorithm based on pseudo-random sequence has high security and encryption efficiency, and can effectively protect the secure transmission of network information.

The inspiration for this book “Industry 5.0: Bridging Humanity and Technology for a Sustainable Future” came from the Hon’ble Governor of Uttarakhand Lt Gen Gurmit Singh, PVSM, UYSM, AVSM, VSM (Retd). He is committed to promote research, innovation, and knowledge exchange that can further contribute to the development of the state and nation. His “One University One Research” initiative is an innovative step for enabling Universities to conduct important research that not only enhances academics but also directly benefits society. Under the program, Uttaranchal University took this opportunity to move forward with “Industry 4.0 and Sustainable Development”. During various meetings, Hon’ble Governor encouraged us to take the outcomes to the next level by sharing the facts with community of academics, policy makers, economists, political leaders, technocrats etc and make them aware about futuristic technological advancements. With this motivation the authors decided to write a book on Industry 5.0, to promote economic expansion, improve technological advancements and solve ethical issues. We are certain that initiatives will help to build a more promising and sustainable future for our country. The world stands at the precipice of a new industrial revolution. We are entering Industry 5.0, a new era that builds on the foundations established by its predecessors but places a greater emphasis on the integration of human intelligence, sustainability, and collaboration. Examining how innovative technologies like the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Extended Reality (XR), and Quantum Computing are revolutionising industries globally, "The Evolution Continues" dives into this shift. This book offers a vision for the future, one in which Industry changes to become more responsive, networked, and environmentally sensitive. The synergy between human knowledge and machine capabilities is at the core of Industry 5.0, promoting a degree of cooperation that guarantees improved results, increased production, and the development of environmentally friendly, sustainable solutions. This book also discusses some of the most important issues and prospects that may arise in the future, such as cybersecurity, cobots' role in human-machine collaboration, and the critical role that edge computing and 5G will play in guaranteeing instantaneous connectivity throughout industrial ecosystems. It integrates the technological, strategic, and human aspects of this revolution as it examines areas with an eye towards the future. It is an exhortation to innovators, educators, technologists, and leaders in the industry to seize the opportunities. Industry 5.0 will be influencing the future with cybersecurity and the Industrial Internet of Things (IIoT), digital twins, metaverse, quantum computing, and sustainable manufacturing, each chapter explores a crucial facet of this shift. Industry 5.0's effects extend beyond the manufacturing floor. It permeates supply chains, education, defence, and even human augmentation, promoting an industrial environment that is human-centric, where employees are empowered, decision-making is improved, and sustainability is taken on the top priority. This book seeks to promote inventions and provide tactical frameworks for integrating technology and humans in a way that benefits organisations, communities, and the environment. Let's welcome technology as we enter this new industrial era, not as a disruptive force but as a means of empowerment, sustainability, and collective advancement.