Internet Of Intelligence Research Articles

Development of Internet of Things and Artificial Intelligence for intelligent sanitation systems: A literature review

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

Combating food waste in the agricultural supply chain: A systematic review of supply chain optimization strategies and their sustainability benefits

Food waste represents a significant global challenge, with approximately one-third of all food produced for human consumption lost or wasted annually, leading to severe environmental, economic, and social consequences. The issue not only exacerbates food insecurity but also contributes to greenhouse gas emissions, waste of resources, and economic losses estimated at over $1 trillion each year. This paper aims to systematically review the causes of food waste across agricultural supply chains and explore effective supply chain optimization strategies to mitigate these losses. Through a comprehensive analysis, the paper identifies critical drivers of food waste, including post-harvest losses, inefficient processing, and aesthetic standards, while proposing technological innovations such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) as solutions to enhance supply chain efficiency. Furthermore, it evaluates policy frameworks that support waste reduction, highlighting the role of government incentives and regulations in promoting sustainable practices. The findings underscore the importance of reducing food waste not only for environmental sustainability but also for economic profitability, as optimizing supply chains can lead to substantial cost savings and increased revenue for businesses. Ultimately, the paper emphasizes the necessity for collaboration among stakeholders—including governments, industries, and non-profit organizations—to create systemic change and effectively combat food waste on a global scale. By fostering innovative practices and policies, we can pave the way for a more sustainable agricultural future, ultimately contributing to the alleviation of food insecurity and the enhancement of economic resilience.

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

A long journey from telemedicine to digital (public) health: the vision of different stakeholders

Abstract The healthcare system willingly adopts new innovative means of communication and solutions offered by computer science. Telemedicine was one of the first terms used in this context. It was initially associated with providing medical services but quickly extended to other areas of health-related activities, including public health. The growth of the global web yielded new prospects for the provision of health services, also resulting in new terms to name the use of information and telecommunication technologies in healthcare. E-health, stemming from the end of 90ies in the 20th century, was initially defined as a new domain strongly associated with public health, especially in the context of access to health information. Quickly, its understanding was extended to all health-related interventions based on innovative digital technologies. Today, digital health and digital public health have emerged as the key terms, signifying the next phase of emerging applications that leverage the advancements made on the technical front. The medical community is probably more inclined to use the former term, while public health professionals are dedicated to the latter. But do these terms carry distinct meanings? Or is it merely a matter of the professional group’s background? Is the focus on individual or population health a differentiating factor? In the meantime, business and technical communities have developed the concept of Health 4.0. This concept emphasizes using technologies like the Internet of Things, cloud computing, big data, and artificial intelligence. However, digital (public) health benefits hugely from these solutions. One can ask whether every professional community needs other concepts and new terms for naming fields, resulting from the multidisciplinary effort. And where are the plain people in this landscape? Do they have sufficient digital and health literacies to accept new modes of health service delivery?

Digitalisation and Cybersecurity: Towards an Operational Framework

As businesses increasingly adopt digital processes and solutions to enhance efficiency and productivity, they face heightened cybersecurity threats. Through a systematic literature review and concept development, this article examines the intersection of digitalisation and cybersecurity. It identifies the methodologies and tools used for cybersecurity assessments, factors influencing the adoption of cybersecurity measures, and the critical success factors for implementing these measures. The article also puts forward the concept of cybersecurity governance process categories, which are used to classify the factors uncovered in the research. Findings suggest that current information security standards tend to be too broad and not adequately tailored to the specific needs of small and medium-sized enterprises (SMEs) when implementing emerging technologies, like Internet of Things (IoT), blockchain, and artificial intelligence (AI). Additionally, these standards often employ a top-down approach, which makes it challenging for SMEs to effectively implement them, as they require more scalable solutions tailored to their specific risks and limited resources. The study thus proposes a new framework based on the Plan-Do-Check model, built around the cybersecurity governance process categories and the three core pillars of governance, culture and standards. This is essentially a bottom-up approach that complements current top-down methods, and will be of value to both information technology (IT) professionals as an operational guide, and to researchers as a basis for future research in this field.

Hollow glass microsphere/polybutadiene composites with low dielectric constant and ultralow dielectric loss in high‐frequency

AbstractHigh‐frequency dielectric materials have been widely and rapidly applied in areas such as automotive radar, Internet of Things, artificial intelligence, and quantum computing. Currently, the challenge in high‐frequency dielectric materials lies in reducing the dielectric constant (Dk) and dielectric loss (Df) without sacrificing its mechanical properties. This study addresses this challenge by introducing air, as the most common “low dielectric factor,” into the polymer matrix in the form of hollow glass microspheres. Meanwhile, the reactive vinyl groups were also introduced onto the surface of the hollow glass microspheres, enabling an interfacial chemical reaction between the side vinyl groups of polybutadiene and its surface so that the organic–inorganic interface compatibility and interface peel strength are simultaneously improved. Consequently, the minimum Dk of 1.29 and Df of 0.0012 in 3–18 GHz are achieved, and the interface peel strength also reaches 0.65 N/mm. Molecular dynamics simulations, analysis of dielectric properties, and interface peel strength reveal the influence of hollow glass microspheres' morphology and chemical structure on their high‐frequency dielectric performance and adhesive strength. This paper provides effective strategies for the structural design and preparation of high‐frequency, low‐dielectric composites, contributing to the further development of next‐generation microwave communication devices towards higher frequencies and faster information transmission.

Implementing Loss Prevention by Identifying Trends and Insights to Help Policyholders Mitigate Risks and Reduce Claims

Loss prevention is a critical focus for both insurers and policyholders, as rising claim volumes lead to increased premiums and financial losses. This paper explores the implementation of loss prevention strategies through the identification of trends and insights derived from historical data, predictive analytics, and emerging technologies. By leveraging these insights, insurers can recommend proactive risk mitigation measures to policyholders, reducing the occurrence of claims and overall risks. The study begins by analyzing historical data trends across various sectors such as auto, property, and health insurance, identifying key factors that drive claims. Predictive analytics is then applied to forecast future risks, allowing insurers to develop personalized strategies for mitigating those risks. Emerging technologies like Internet of Things (IoT) devices and artificial intelligence (AI) are highlighted for their role in providing real-time data and early warning systems, which help to prevent incidents before they escalate into claims. This paper also outlines practical loss prevention strategies for policyholders, such as installing smart home devices to monitor potential hazards, encouraging safe driving habits through telematics, and promoting health and wellness programs to lower medical claims. These risk mitigation strategies are shown to provide significant return on investment (ROI) for policyholders by reducing claims frequency and severity. Through case studies and data analysis, the paper demonstrates that proactive loss prevention not only benefits policyholders by lowering their risk exposure but also helps insurers reduce claims costs and enhance profitability. In conclusion, this research highlights the importance of trend identification, predictive analytics, and emerging technologies in the future of loss prevention, offering a path forward for insurers and policyholders to work collaboratively in mitigating risks.

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

Investigation of the Versatile Utilization of Three-Dimensional Vertical Resistive Random-Access Memory in Neuromorphic Computing.

The continuous advancement of computing technologies such as the Internet of Things and artificial intelligence emphasizes the need for innovative approaches to data processing. Faced with limitations in processing speed due to the exponentially increasing volume of data, conventional von-Neumann computing systems are transforming into a new paradigm called neuromorphic computing, inspired by the efficiency of the human brain. We fabricate three-dimensional vertical resistive random-access memory (VRRAM), which is highly suited for neuromorphic computing, and demonstrate its value as an artificial synapse. Beyond simulating simple synaptic functionalities such as spike-rate-dependent plasticity, spike-timing-dependent plasticity, and paired-pulse facilitation, we propose specific applications and experimentally implement them. In pattern recognition simulations based on the weight update characteristics of the fabricated VRRAM, the accuracy achieved in pattern recognition using appropriate pulse schemes reaches 90.4%. Additionally, we demonstrate adaptive learning behavior on the device by mimicking Pavlov's dog experiment with combinations of applied voltage pulses. Finally, we employ suitable write/erase pulse trains to implement binary representations for decimal numbers ranging from 0 to 15, thereby illustrating the significant potential of local devices for edge computing applications.

Application of Internet of Things and Artificial Intelligence for Risk Mitigation of Accidents in Construction Sites: A Case Study of Brazilian Construction Companies

The construction industry constantly faces challenges related to workplace safety. Managing work safety is crucial to ensuring worker protection and successful project completion. In this context, this study investigates the applicability of Internet of Things technologies and Artificial Intelligence models, such as YOLO, to enhance safety on construction sites. The research identifies significant cultural resistance to the adoption of disruptive technologies in the Brazilian construction sector, and interviews with industry professionals reveal a low level of awareness and interest in these technological innovations. Through case studies and quantitative analyses, the study demonstrates YOLO's potential in identifying risks, such as detecting PPE and structural hazards. The results highlight the need to overcome conservative barriers and invest in technological solutions to increase safety and efficiency in construction.

Healthiness and Safety of Smart Environments through Edge Intelligence and Internet of Things Technologies

Smart environments exploit rising technologies like Internet of Things (IoT) and edge intelligence (EI) to achieve unseen effectiveness and efficiency in every tasks, including air sanitization. The latter represents a key preventative measure–made even more evident by the COVID-19 pandemic–to significantly reduce disease transmission and create healthier and safer indoor spaces, for the sake of its occupants. Therefore, in this paper, we present an IoT-based system aimed at the continuous monitoring of the air quality and, through EI techniques, at the proactively activation of ozone lamps, while ensuring safety in sanitization. Indeed, these devices ensure extreme effectiveness in killing viruses and bacteria but, due to ozone toxicity, they must be properly controlled with advanced technologies for preventing occupants from dangerous exposition as well as for ensuring system reliability, operational efficiency, and regulatory compliance.

Retraction Note: Research on key technologies of data security and privacy protection in internet of things group intelligence

Retraction Note: Research on key technologies of data security and privacy protection in internet of things group intelligence

Low power process tolerant nano cache memory for military applications

ABSTRACT Rapid growth in high-performance battery-powered computing applications, Internet of Things (IoT) and artificial intelligence (AI), has raised the need for low-power integrated circuits. Particularly in military applications, wireless sensor nodes are used to detect border intrusion, weapon attacks, biometric wearables, internet of battlefield things (IoBT), etc. The computing and security devices utilised by the soldiers are powered by batteries. To increase the life and performance of these devices, the integrated circuits (IC) used within these devices need careful design. Particularly, an appropriate memory needs more attention because the design of memory has a substantial impact on IC performance as it occupies most of the chip space. In this article, a novel 8T transistor low-power cache memory cell is proposed and simulations for nominal chirality and dual chirality are done using HSPICE-compatible CNFET (carbon nanotube field effect transistor) technology. The power consumption is minimized in dual chirality case than nominal chirality. But still the power saving percentage of the proposed cache memory cell remains good compared to conventional memory structures. Thus the proposed cache memory cell provides low power, delay and energy apart from being process tolerant, thus proving that it is compatible for warfare and military applications.

Revolutionizing Inventory Management: The Role of Data Mining in Industry 4.0

In the era of Industry 4.0, with the rapid development and application of technologies such as the Internet of Things (IoT), Big Data and Artificial Intelligence (AI), the manufacturing industry is undergoing unprecedented changes. In this context, data mining technology has become integral to inventory management practices in all industries. This study examines the profound impact of data mining on inventory management efficiency. By leveraging advanced analytics and machine learning algorithms, data mining enables organizations to accurately forecast demand, optimize inventory levels, and improve supply chain transparency. Through real-world case studies and comprehensive analysis, this research highlights how data mining techniques, such as central object-based clustering algorithms, can be successfully applied to optimize material classification, warehouse space utilization and operational efficiency. In addition, this study explores the broader applications of data mining beyond inventory management, including marketing, financial risk management, healthcare, transportation, social media, and cybersecurity. Overall, this study provides valuable insights into how data mining can reshape inventory management practices and drive business growth in the digital age.

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

Application of AI and IoT in Traffic Management of Large Metropolitan Cities

Urban traffic congestion is a growing problem worldwide, leading to wasted time, fuel, and increased emissions. To address this challenge, smart traffic management systems powered by the Internet of Things (IoT) and Artificial Intelligence (AI) are emerging as a promising solution. This paper explores how these technologies can be combined to create a more efficient and sustainable traffic management system for large cities.

Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review

The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things (IoT) technologies have driven the innovation of advanced autonomous driving systems (ADSs). Image classification deep learning (DL) algorithms immensely contribute to the decision-making process in ADSs, showcasing their capabilities in handling complex real-world driving scenarios, surpassing human driving intelligence. However, these algorithms are vulnerable to adversarial attacks, which aim to fool them in real-time decision-making and compromise the reliability of the autonomous driving functions. This systematic review offers a comprehensive overview of the most recent literature on adversarial attacks and countermeasures on image classification DL models in ADSs. The review highlights the current challenges in applying successful countermeasures to mitigating these vulnerabilities. We also introduce taxonomies for categorizing adversarial attacks and countermeasures and provide recommendations and guidelines to help researchers design and evaluate countermeasures. We suggest interesting future research directions to improve the robustness of image classification DL models against adversarial attacks in autonomous driving scenarios.

Smart Agriculturing Based on KSK Approach: A Novel AI-Driven-IoT(AIIoT) Based Decision-Making Approach

An enormous change is taking place in the agriculture industry as a result of the introduction of the Internet of Things (AIIoT), which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The purpose of this research is to present a comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making. The concept of smart agricultural decision-making refers to a breakthrough method that enables farmers to optimise their farm operations while also making decisions that are well-informed. Farmers are able to increase crop yields while simultaneously reducing costs and hazards when they make use of advanced analytics, real-time data, and decision-making tools. The development of smart agriculture will result in farmers having increased decision-making authority, which will ultimately lead to an agricultural sector that is more sustainable and productive. The Internet of Things (IoT) and artificial intelligence (AI) in smart agriculture is a cutting-edge technology that has the potential to be a game-changer in terms of how we produce and consume food. Farmers may be able to improve agricultural yields and quality, streamline operations, and contribute to a food supply chain that is more efficient and sustainable if they use artificial intelligence and the internet of things. In spite of the fact that there are still certain problems that need to be cleared up, the Internet of Things (IoT) offers numerous advantages for smart agriculture, and its implementation is anticipated to increase in the years to come. The KSK technique, also known as the Knowledge-Sensors-Knowledge approach, is a suggestion made by Dr. Kutubuddin S. Kazi, who is also using his name. The output of the KSK technique results in an accuracy of 99.9% and a recall of 97.9%, respectively.

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

SIEMES DIGITAL TRANSFORMATION CASE STUDY TOWARDS ORGANIZATIONAL SCALE AND SUCCESS IN ELECTRICAL ENGINEERING INDUSTRY”

This study investigates a range of scaling strategies for electrical engineering firms, with a focus on various crucial aspects such as internal innovation, strategic regional expansion, geographical proximity, and collaborative partnerships. Internal innovation is highlighted as a key driver of growth, as it allows firms to develop new technologies and processes that can differentiate them from competitors. Strategic regional expansion is discussed as a means to tap into new markets and leverage local opportunities, while geographical proximity is examined for its impact on operational efficiency and customer relationships. Collaborative partnerships are also emphasized for their role in enhancing capabilities and driving mutual benefits. The case study of Siemens AG serves as a prominent example, showcasing how digital technologies like the Internet of Things (IoT), artificial intelligence (AI), and cloud computing have significantly transformed their approach to scaling. Siemens AG's experience underscores how these technologies can support internal innovation, streamline regional expansion efforts, overcome geographical barriers, and foster effective collaborations. The article proposes a detailed framework in which digital transformation is central to driving these strategies. It argues that digital technologies are not just enablers but essential components that fuel innovation across various domains. Additionally, digital transformation is posited as a critical factor in facilitating smoother and more effective expansion into new regions, bridging geographical gaps that can otherwise impede growth, and enhancing collaborative efforts both within and outside the organization. By integrating these elements, businesses can achieve sustainable growth and maintain a competitive edge in an ever-evolving and challenging market landscape.is considered near the evaluation of efficiency of management an enterprise. Evaluation technology is realized on the basis of comparison of financial indexes, in particular to the analysis of indexes of profitability and analysis of business activity