Search Results (47)

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management. Full article

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article

Respirable crystalline silica is a well-established occupational hazard in construction work. Despite increased awareness, consistent exposure control remains a challenge, particularly in dynamic and resource-constrained environments. Respirable crystalline silica exposure in construction environments challenges the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth). Respirable crystalline silica particles cause severe health complications, including silicosis, lung cancer, cardiovascular diseases, and autoimmune disorders, representing a significant barrier to achieving SDG 3.9’s target of reducing deaths and illnesses from hazardous chemical exposures by 2030. This systematic review evaluates two decades of advancements (2004–2024) in respirable crystalline silica identification, characterisation, and mitigation within construction, synthesising evidence from 143 studies to assess progress toward sustainable occupational health management. This review documents a paradigmatic shift from traditional exposure assessment toward sophisticated monitoring approaches incorporating real-time detection systems, virtual reality–Computational Fluid Dynamics simulations, and wearable sensor technologies. Engineering controls, including local exhaust ventilation, wet suppression methods, and modified tool designs, have achieved exposure reductions exceeding 90%, directly supporting SDG 8.8’s commitment to safe working environments for all workers, including migrants and those in precarious employment. However, substantial barriers persist, including prohibitive costs, inadequate infrastructure, and regional regulatory disparities that particularly disadvantage lower-resourced countries, contradicting the Sustainable Development Goals’ principles of leaving no one behind. The findings advocate holistic approaches integrating technological innovation with context-specific regulations, enhanced international cooperation, and culturally adapted worker education to achieve equitable occupational health protection supporting multiple Sustainable Development Goals’ objectives by 2030 and also highlighting potential areas for future research. Full article

The increased usage of smart sensors has introduced both opportunities and complexities in managing residential energy consumption. Despite advancements in sensor data analytics and machine learning (ML), existing energy management systems (EMS) remain limited in interpretability, adaptability, and user engagement. This paper presents EnergiQ, an intelligent, end-to-end platform that leverages sensors and Large Language Models (LLMs) to bridge the gap between technical energy analytics and user comprehension. EnergiQ integrates smart plug-based IoT sensing, time-series ML for device profiling and anomaly detection, and an LLM reasoning layer to deliver personalized, natural language feedback. The system employs statistical feature-based XGBoost classifiers for appliance identification and hybrid CNN-LSTM autoencoders for anomaly detection. Through dynamic user feedback loops and instruction-tuned LLMs, EnergiQ generates context-aware, actionable recommendations that enhance energy efficiency and device management. Evaluations demonstrate high appliance classification accuracy (94%) using statistical feature-based XGBoost and effective anomaly detection across varied devices via a CNN-LSTM autoencoder. The LLM layer, instruction-tuned on a domain-specific dataset, achieved over 91% agreement with expert-written energy-saving recommendations in simulated feedback scenarios. By translating complex consumption data into intuitive insights, EnergiQ empowers consumers to engage with energy use more proactively, fostering sustainability and smarter home practices. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article

The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents. Full article

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article

The concept of a digital twin (DT), rooted in mid-20th-century ideas, has recently gained significant traction even outside software simulation and engineering modeling. The recent advancements in computational power and the development of model integration methodologies have enabled the creation of virtual replicas of complex physical objects. The success of DTs in engineering has also pushed for the exploration of their use in other domains, especially where complex systems are at stake. One of these cases, which is the focus of this paper, is the modeling of cities and the way they are transformed via technologies into so-called smart cities. In these systems, the huge amount of data that are made accessible and constantly updated via sensor networks suggests that one can use DTs dedicated to the urban scenario as data-driven decision-making devices. However, the concept of a DT was not developed for socio-technical systems and requires careful analysis when applied to urban scenarios. While technologies and information systems have become integrated into city management, this has not reduced the complexity of the city. Relying only on sensory data for city modeling and management seems pretentious since detectable data (what is made accessible via sensor networks) do not seem suitable to inform on all important aspects of the city. Urban DTs hold promise, yet their development necessitates careful consideration of both opportunities and limitations. For this goal, it can be helpful to exploit an ontological analysis due to its neutral and systematic approach and to look at a city as a system of intertwined relationships across its components, such as places, agents, and knowledge. The variety of interactions that the components manifest highlights aspects of the city that the type of data we can collect today leaves unexplored. The paper presents a preliminary example of this issue by studying cases of city squares. The final part of this paper is a call to analyze DTs’ potential role in urban contexts and become aware of the intrinsic limitations of the data they rely upon. Full article

Smart homes have become an important part of our daily lives, changing our habits to make them easier to live with in a sustainable way. This study highlights a context-sensitive system that continuously adapts to the user’s current activities and physiological habits in order to preserve physical and mental health while achieving sustainability goals. The system uses Internet of Things (IoT) sensors and smart home devices to measure indicators such as physical activity, heart rate, stress levels, and sleep quality. Based on these real-time measurements, the device offers personalized recommendations for a healthier lifestyle, such as physical activity reminders, stress management techniques, and sleep quality adjustments. By balancing the novelty and precision of its recommendations, the model aims to actively involve users without overloading them, thus promoting gradual and lasting behavioral changes. The architecture also incorporates multi-criteria evaluation measures, including accuracy and novelty-based diversity, to ensure an optimized user experience that is both accurate and adaptable. This paper proposes an advanced recommendation system for enhanced health monitoring by integrating multi-criteria decision-making and contextual awareness in order to have multi-objective results. The proposed system makes the personal recommendations with dynamic user categorization, using different kinds of notifications: reminder to exercise, monitoring heart health, and how to handle the stress. This approach is targeted to be scalable and adaptive to real-world conditions to enhance the overall effectiveness of health recommendations and strategies for preventive healthcare. The use of IoT will help in presenting a sound framework for personalized health interventions, enabling user engagement and better health outcomes. Full article

To meet the 2050 targets about climate change and decarbonization, accomplishing thermal comfort, Internet of Things (IoT) ecosystems are key enabling technologies to move the Built Environment (BE) towards Smart Built Environment (SBE). The first contributions of this paper conceptualise SBE from its dynamic and adaptative perspectives, considering the human habitat, and enunciate SBE as a multidimensional approach through six ways of inhabiting: defensive, projective, scientific, thermodynamic, subjective, and complex. From these premises, to analyse the performance indicators that characterise these multidisciplinary ways of inhabiting, an IoT-driven methodology is proposed: to deploy a sensor infrastructure to acquire experimental measurements; analyse data to convert them into context-aware information; and make knowledge-based decisions. Thus, this work tackles the inefficiency and high energy consumption of public buildings with the challenge of balancing energy efficiency and user comfort in dynamic scenarios. As current systems lack real-time adaptability, this work integrates an IoT-driven approach to enhance energy management and reduce discrepancies between measured temperatures and normative thresholds. Following the energy efficiency directives, the obtained results contribute to the following: understanding the complexity of the SBE by analysing its thermal performance, quantifying the potential of energy saving, and estimating its economic impact. The derived conclusions show that IoT-driven solutions allow the generation of real-data-based models on which to enhance SBE knowledge, by increasing energy efficiency and guaranteeing user comfort while minimising environmental effects and economic impact. Full article

Protecting cultural heritage buildings poses significant research challenges. Effective damage prevention hinges on a thorough understanding of structural behavior and the continuous monitoring of its changes over time. Advanced visualization tools are essential to provide adequate awareness of the monitoring systems installed over the years while guaranteeing a quick, basic analysis of their data. This paper addresses a crucial gap in structural health monitoring (SHM), particularly in managing complex structures and systems, by responding to the growing need for tools that not only represent 3D models enriched with heterogeneous data and metadata but also facilitate detailed analysis of sensor recordings. In response to this challenge, it proposes the integration of a 3D informational model and an interactive web-based platform for monitoring data, creating a comprehensive management tool. Piazza del Duomo UNESCO Site in Pisa serves as an ideal test case due to its historical significance, structural complexity, and the wealth of monitoring data collected over time. With their interactive architecture, the two developed integrated visualization tools that could offer an effective solution for data management and visualization in other heritage contexts, particularly in cases where the monitoring system consists of numerous sensors and has evolved substantially over the years. Full article

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article

Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients’ personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user’s smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses. Full article