This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet Research and Experimentation initiative of the European Commission and represents a unique in the world city-scale experimental research facility. Additionally, this facility supports typical applications and services of a smart city. Tangible results are expected to influence the definition and specification of Future Internet architecture design from viewpoints of Internet of Things and Internet of Services. The facility comprises a large number of Internet of Things devices deployed in several urban scenarios which will be federated into a single testbed. In this paper the deployment being carried out at the main location, namely Santander city, is described. Besides presenting the current deployment, in this article the main insights in terms of the architectural design of a large-scale IoT testbed are presented as well. Furthermore, solutions adopted for implementation of the different components addressing the required testbed functionalities are also sketched out. The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions.
Jerifa Zaman, Atefeh Shoomal, Mohammad Jahanbakht
et al.
The objective of this study is to conduct an analysis of the scientific literature on the application of the Internet of Things (IoT) and artificial intelligence (AI) in enhancing supply chain operations. This research applies a dual approach combining bibliometric analysis and topic modeling to explore both quantitative citation trends and qualitative thematic insights. By examining 810 qualified articles, published between 2011 and 2024, this research aims to identify the main topics, key authors, influential sources, and the most-cited articles within the literature. The study addresses critical research questions on the state of IoT and AI integration into supply chains and the role of these technologies in resolving digital supply chain management challenges. The convergence of IoT and AI holds immense potential to redefine supply chain management practices, improving productivity, visibility, and sustainability in interconnected global supply chains. This research not only highlights the continuous evolution of the supply chain field in light of Industry 4.0 technologies—such as machine learning, big data analytics, cloud computing, cyber–physical systems, and 5G networks—but also provides an updated overview of advanced IoT and AI technologies currently applied in supply chain operations, documenting their evolution from rudimentary stages to their current state of advancement.
Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments.
This paper explores the application of the ESP32 microcontroller in edge computing, focusing on the design and implementation of an edge server system to evaluate performance improvements achieved by integrating edge and cloud computing. Responding to the growing need to reduce cloud burdens and latency, this research develops an edge server, detailing the ESP32 hardware architecture, software environment, communication protocols, and server framework. A complementary cloud server software framework is also designed to support edge processing. A deep learning model for object recognition is selected, trained, and deployed on the edge server. Performance evaluation metrics, classification time, MQTT (Message Queuing Telemetry Transport) transmission time, and data from various MQTT brokers are used to assess system performance, with particular attention to the impact of image size adjustments. Experimental results demonstrate that the edge server significantly reduces bandwidth usage and latency, effectively alleviating the load on the cloud server. This study discusses the system’s strengths and limitations, interprets experimental findings, and suggests potential improvements and future applications. By integrating AI and IoT, the edge server design and object recognition system demonstrates the benefits of localized edge processing in enhancing efficiency and reducing cloud dependency.
Biantoro Agung W., Majid R.B. Abdul, Vidayanti D.
et al.
This research presents the design and development of a Smart Rainwater Harvesting (RWH) system integrated into a green building using hybrid energy sources—solar and wind—supported by Internet of Things (IoT) technology. This study used the VDI 2221 engineering design methodology to systematically analyze, select, and develop a suitable RWH system design powered by solar cells and wind energy. The main objective of this research was to conduct a comparative analysis of various RWH tool designs to identify the most efficient and sustainable configuration to be applied in coastal and deltaic environments. This research method employed quantitative analysis and VDI 2221 analysis, a method for engineering product development developed by the Verein Deutscher Ingenieure (VDI), the German Association of Engineers. This method has become an international standard in engineering design, especially in the fields of industrial engineering and in civil systems engineering. The research results indicate that there are three RWH design options for coastal areas, namely Model 1, Model 2, and Model 3. The best choice is Model 1, which utilises sturdy, rust-resistant, and wind-resistant materials and incorporates renewable energy and IoT technology. The wind power plant is capable of producing 32.42 kWh/day of electricity, while the harvested rainwater ranges from 43 mm to 1043 mm per month. A comprehensive design concept that aligns with green building standards and is feasible to implement an innovative and sustainable rainwater harvesting system.
Nurdaulet Tasmurzayev, Bibars Amangeldy, Timur Imankulov
et al.
Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology.
The expansion of Internet of Things (IoT) networks has enabled real-time data collection and automation across smart cities, healthcare, and agriculture, delivering greater convenience and efficiency; however, exposure to diverse threats has also increased. Machine learning-based Intrusion Detection Systems (IDSs) provide an effective means of defense, yet they require large volumes of data, and the use of raw IoT network data containing sensitive information introduces new privacy risks. This study proposes a novel privacy-preserving synthetic data generation model based on a tabular diffusion framework that incorporates Differential Privacy (DP). Among the three diffusion models (TabDDPM, TabSyn, and TabDiff), TabDiff with Utility-Preserving DP (UP-DP) achieved the best Synthetic Data Vault (SDV) Fidelity (0.98) and higher values on multiple statistical metrics, indicating improved utility. Furthermore, by employing the DisclosureProtection and attribute inference to infer and compare sensitive attributes on both real and synthetic datasets, we show that the proposed approach reduces privacy risk of the synthetic data. Additionally, a Membership Inference Attack (MIA) was also used for demonstration on models trained with both real and synthetic data. This approach decreases the risk of leaking patterns related to sensitive information, thereby enabling secure dataset sharing and analysis.
The Internet of Things (IoT) is one of the biggest technical advances in recent years, improving our lives in many different ways. One important area of its application is sustainable development. Additionally, funds’ availability is as crucial for sustainable development as IoT. The relationship between technological advancements like big data, blockchain, artificial intelligence (AI), mobile platforms, and the IoT with finance is referred to as “digital finance”. The financial system has been digitalized for a while now. The capacity to quickly, accurately, affordably, and conveniently access vast amounts of complex data related to investments and sustainability consequences accelerates transparency and helps public institutions monitor the regulatory aspects of sustainable development. This study aims to investigate the characteristics of prior studies to comprehend the most recent developments in IoT and sustainable finance. A bibliometric analysis is performed on 306 research publications retrieved from the Scopus database and published between 2011 and 2024. Software tools like VOS-Viewer and Biblioshiny with R Studio are used for the analysis. The study is capable to summarise the traits and patterns of IoT and sustainable finance research. Moreover, the research identifies well-known authors, journals, and institutions and finds the research articles with the highest citation counts and the fastest-growing theme of the domain. This paper offers insightful recommendations to academicians for their future research.
Shah Abdul Latif, Micheal Drieberg, Sohail Sarang
et al.
The Wireless Sensor Networks (WSNs) enabled Internet of Things (IoT) applications face energy efficiency challenge due to the limited battery capacity of the sensor nodes. Hence, the network’s performance often involves a tradeoff with network lifetime. Traditional medium access control (MAC) protocols are less adaptable to the dynamic network conditions. While existing reinforcement learning (RL) based MACs are more adaptable, they still encounter challenges such as complexity and dimensionality. Therefore, this work aims to develop an RL based intelligent Duty cycle MAC (RiD-MAC) protocol that incorporates suitable network information to balance complexity and performance, effectively. The proposed RiD-MAC protocol is based on the Q-learning algorithm, meticulously designed with remaining energy as the state space and duty cycle as the action space. The reward is then formulated based on energy consumption and throughput. It is implemented on OMNeT++ platform-based Castalia simulator and the performance is compared with three state-of-the-art protocols, including AQSen-MAC, rlDC-MAC and QX-MAC under three simulation scenarios, stationary nodes with periodic traffic, hybrid traffic and node mobility. The simulation results demonstrate that RiD-MAC protocol significantly improves energy efficiency, with reduction in receiver energy consumption of up to 21%, and receiver energy consumption per bit of up to 26%, when compared to state-of-the-art protocols.
The significance of V2X (Vehicle-to-Everything) technology in the context of highly automated and autonomous vehicles can hardly be overestimated. While V2X is not considered a standalone technology for achieving high automation, it is recognized as a safety-redundant component in automated driving systems. This article aims to systematically assess the requirements towards V2X input data to highly automated and autonomous systems that can individually, or in combination with other sensors, enable certain levels of autonomy. It addresses the assessment of V2X input data requirements for different levels of autonomy defined by SAE International, regulatory challenges, scalability issues in hybrid environments, and the potential impact of Internet of Things (IoT)-based information in non-automotive technical fields. A method is proposed for assessing the applicability of V2X at various levels of automation based on system complexity. The findings provide valuable insights for the development, deployment and regulation of V2X-enabled automated systems, ultimately contributing to enhanced road safety and efficient mobility.
Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.
Computer applications to medicine. Medical informatics
Non-orthogonal multiple access (NOMA) technology is projected to significantly increase the spectrum efficiency of the fifth-generation and subsequent wireless networks. Holographic reconfigurable Intelligent surfaces (HRISs) are a revolutionary technology that can deliver excellent spectral and energy efficiency at a cheap cost in wireless networks. In this letter, we investigate the short-packet communication (SPC) with the NOMA-based HRIS system with the internet of things (IoT). A base station (BS) communicates with two NOMA users by using HRIS in the proposed system to enhance spectral efficiency. Furthermore, we derived the exact closed-form expression of the average block error rate (BLER) for two NOMA users. To get more insight into the proposed system, the asymptotic BLER analysis was also carried out at high signal-to-noise ratio regime. The numerical results validate the current analysis and show that the presented NOMA strategy exceeds orthogonal multiple access-based approaches in terms of BLER and throughput.
Shahram Samadi, Sepehr Samadi, Behnam Samadi
et al.
With the rising attention towards improving the quality of life and mental health, sleep hygiene and sleep quality have recently been the main topics of numerous studies. Quality of sleep not only affects our physical status but also plays a pivotal role in our psychological and emotional states. Sleep deprivation can increase the risk of cardiovascular and metabolic diseases along with the risk of impaired concentration and consequent road injury and accidents. As technology has become a main figure in our daily lives, technological advances have paid a great interest in improving the quality of sleep by enhancing the detection of sleep-related disorders and sleep abnormalities, particularly in the setting of smart homes and the Internet of Things (IoT). Smartphone applications, portable wearable gadgets, and devices along with more sophisticated and precise algorithms are now endeavoring to help us improve our quality of sleep and subsequently our quality of life. Hence, this review aims to illustrate a vivid picture of recent advancements in smart homes and their related technologies for improving sleep quality.
Alagele Zaman Raad Hammadi, Alkafaje Shuhub Ahmed Malik, Jabar Ruaa Satar
Modern world technologies such as the integration of technologies such as the Internet of Things (IoT), cloud computing, and machine learning (ML) enhance the challenges of smart industrial management. Detecting anomalies in predictive maintenance within smart factories, and monitoring machine health to prevent unexpected breakdowns. This research presents an advanced model for designing automatic encoders capable of distinguishing between sounds emitted by machines in industrial environments and identifying faults. The MIMII dataset and advanced feature extraction techniques, such as MFCCs, are adopted as key factors in making the proposed model. The four evaluation measures: accuracy, recall, recall, and F1 score, in addition to the confusion matrix, were also adopted. To evaluate the model's performance. The results confirm the effectiveness and robustness of the proposed deep neural network model designed for autoencoders in the field of artificial audio classification. With a commendable accuracy rate of 93.95% and F1 score of 95.31%,