Search Results (3,846)

Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve the automation of electrical appliances and optimize electricity utilization. This paper describes the implementation of a smart switch with extended facilities compared to traditional switches, such as visual indication of evacuation routes in case of fire and acoustic alerts for emergencies. The proposed embedded system implements Modbus RTU serial communication to receive information from a fire alarm-control panel. An extension to the Modbus communication protocol, called Modbus Extended (ModbusE), is also proposed for smart switches and emergency switchboards. The embedded smart switch described in this paper as a scientific and practical contribution in this field, based on a performant microcontroller system, is integrated into the Building Internet of Things (BIoT) concept and uses the innovative ModbusE protocol. The proposed smart lighting system integrates building lighting access control for smart switches and sockets and can be extended to incorporate functionality for smart thermostats, access control and smart sensor-based information acquisition. Full article

To guarantee the reliability of Tactile Internet (TI) applications such as telesurgery, which demand extremely high reliability and are experiencing rapid expansion, we propose a novel smart resilience mechanism for Next-Generation Ethernet Passive Optical Networks (NG-EPONs). Our architecture integrates Artificial Intelligence (AI) and Software-Defined Networking (SDN)-Enabled Broadband Access (SEBA) platform to proactively enhance network reliability and performance. By harnessing the AI’s capabilities, our system automatically detects and localizes fiber faults, establishing backup communication links using Radio Frequency over Glass (RFoG) to prevent service disruptions. This empowers NG-EPONs to maintain uninterrupted, high-quality network service even in the face of unexpected failures, meeting the stringent Quality-of-Service (QoS) requirements of critical TI applications. Our AI model, rigorously validated through 5-fold cross-validation, boasts an average accuracy of 81.49%, with a precision of 84.33%, recall of 78.18%, and F1-score of 81.00%, demonstrating its robust performance in fault detection and prediction. The AI model triggers immediate corrective actions through the SDN controller. Simulation results confirm the efficacy of our proposed mechanism in terms of delay, system throughputs and packet drop rate, and bandwidth waste, ultimately ensuring the delivery of high-quality network services. Full article

This article describes the development and implementation of a water resource management system utilizing open technologies such as the ESP32 microcontroller and MicroPython. This system stands out for its low cost, high efficiency and adaptability to various environments, thanks to the integration of free or low-cost communications such as LoRaWAN and NB-IoT, as well as the use of open-source programming, which offers flexibility. The article details the use of JSN-SR04T ultrasonic sensors, manufactured by JINZHAN, a company based in China, for water-level measurement and the use of 3D printing to manufacture customized components, demonstrating a scalable and replicable solution for efficient water management. Full article

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. Full article

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system. Full article

In this paper, we present the development and evaluation of a contextually relevant, cost-effective, multihop cluster-based agricultural Internet of Things (MCA-IoT) network. This network utilizes commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) and LoRa communication technologies, along with the Raspberry Pi 3 Model B+ (RPi 3 B+), to address the challenges of climate change-induced global food insecurity in smart farming applications. Employing the lean engineering design approach, we initially implemented a centralized cluster-based agricultural IoT (CA-IoT) hardware testbed incorporating BLE, RPi 3 B+, STEMMA soil moisture sensors, UM25 m, and LoPy low-power Wi-Fi modules. This system was subsequently adapted and refined to assess the performance of the MCA-IoT network. This study offers a comprehensive reference on the novel, location-independent MCA-IoT technology, including detailed design and deployment insights for the agricultural IoT (Agri-IoT) community. The proposed solution demonstrated favorable performance in indoor and outdoor environments, particularly in water-stressed regions of Northern Ghana. Performance evaluations revealed that the MCA-IoT technology is easy to deploy and manage by users with limited expertise, is location-independent, robust, energy-efficient for battery operation, and scalable in terms of task and size, thereby providing a versatile range of measurements for future applications. Our results further demonstrated that the most effective approach to utilizing existing IoT-based communication technologies within a typical farming context in sub-Saharan Africa is to integrate them. Full article

This paper explores the role that 5G, WiFi 7, and Time-Sensitive Networking (TSN) play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. It provides an in-depth analysis of each technology’s application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, this paper includes a comparative study of these technologies, analyzing them based on several industrial use cases, supported secondary applications, industry adoption, and current market trends. This paper concludes by highlighting the challenges and future directions for adopting these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing. Full article

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, cloud computing, sensors, and communications with the work from many varied disciplines (e.g., civil engineering, architecture, etc.), mixed with aspects of environmental and business sustainability, makes the management of these projects really challenging. Reports forecast a scarcity of qualified candidates, given this complexity and the growth of activity in SC projects. The European project SMACITE has addressed the requirements of the qualification of an ICT workforce with an analysis of multiples sources of information from the labor market, feedback from involved stakeholders, and the literature. The goal was the development of two occupational ICT profiles as a reference for training and for the availability of candidates for job vacancies. The result is two ICT role profiles for engineers and technicians, mapped with the European skills frameworks ESCO and EN16234. The profiles determined the whole set of requirements, including not only the technical areas and soft skills, but also additional technical areas and sustainability and managerial skills and the analysis of different sources of information. Our work has also determined which existing ESCO occupations are similar to the two reference profiles, so they are better adapted to SC projects. The training activities of SMACITE have also suggested the amount of training expected for a varied sample of candidates who want to be qualified for SC projects. Full article

The aim of the paper was to identify which European capitals are sustainable and smart, why, and what influences the ranking. The main research hypothesis was to indicate that cities in the ‘old’ E.U. countries (richer and with higher levels of economic development) are more sustainable and smart. Furthermore, sustainable smart cities, by definition, through the use of advanced and modern management tools and technological support, should contribute to community resilience. Sustainable energy plays a significant role in the measurement system. The study’s results showed the differences that exist across countries, as well as the leaders in each smart category and area. This is interesting and new; from a research point of view, there has been no study based on OECD research and data confronting and correlating the range of data with indicators found in the literature. The study results show that the concept of a smart city is comprehensive and that it is necessary to analyze in depth the various sub-categories included in the measurement and assessment of smartness offered by different indicators. This is because it turns out that an overall score and ranking do not always mean that a city is smart in every area and every element included in smart. Statistical methods and literature analysis are used for the study. The results represent a novel development and contribution to the science discipline and can be the basis for further scientific exploration in this area. The research gap and challenge indicate whether there is a link and correlation between the use of sustainable energy in E.U. countries and the implementation of smart concepts in European capitals in the context of the division into ‘new’ and ‘old’ E.U. capitals. An important element is the verification of the thesis that ‘old’ capitals are more advanced in the implementation of smart cities and make greater use of sustainable energy to meet social and economic needs. The thesis has been partly falsified and confirmed negatively; the results are not obvious. It means that the ‘new’ E.U. countries are very skillful in using financial, organizational, and common development policy opportunities to make their cities modern, intelligent, and friendly to their inhabitants. Full article

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats. Full article

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R² value. Full article

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks. Full article

This paper focuses on studying the concept of walkability in Riyadh, Saudi Arabia, after the implementation of the Humanizing Neighborhoods Initiative. Al-Falah neighborhood was selected as a case study because it was one of the first neighborhoods to implement walkability strategies. By adopting a qualitative critical approach, this research collected data through a literature review and on-site observation to assess walkability in the neighborhood. Analysis of the observations highlighted the challenges and opportunities of walkability in Riyadh and the wider Gulf cities. Recommendations for promoting walkable communities in Riyadh include enhancing pedestrian infrastructure, incorporating mixed-use developments, integrating green infrastructure, implementing smart urban planning principles, and integrating public transportation systems. This paper emphasizes the importance of community engagement, preserving local identity, and involving various stakeholders to transform Riyadh into a pedestrian-friendly city that prioritizes public health and sustainability. Full article

Urbanization has driven the growth of high-rise residential areas, creating unique challenges for enhancing residents’ well-being, especially in large metropolitan regions. This study investigated the impact of smart green spaces—green areas integrated with technology to optimize environmental benefits—on environmental awareness, social cohesion, and life satisfaction in these dense urban environments. Utilizing data collected from questionnaires and field interviews in seven representative high-rise residential areas in Guangzhou, China, the structural equation model (SEM) was employed to explore the complex effects of smart green spaces. The findings reveal positive correlations between the presence, accessibility, and technological features of smart green spaces, which contribute to heightened environmental awareness, strengthened social bonds among residents, and increased life satisfaction. These results emphasize the diverse benefits of urban green spaces enhanced by smart technologies, beyond their aesthetic and recreational roles. This study suggests that strategic planning and policy initiatives focused on the development and operation of smart green spaces can substantially improve urban residents’ well-being by fostering environmental consciousness, promoting community interaction, and enhancing the overall quality of life in high-rise residential settings. These insights are crucial for urban planners, policymakers, and community stakeholders, providing a blueprint for leveraging smart green spaces in creating sustainable, resilient, and livable urban environments. Full article

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities. Full article