Search Results (965)

Due to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To address this, an adaptive smart energy-saving campus system is developed in this study to improve students’ electricity usage habits. In this system, the Internet of Things (IoT) with control interfaces is integrated to enhance convenience. Using expert system rules, the system regulates the operation of the IoT for the efficient energy-saving control of a classroom. Additionally, by incorporating a random forest classifier, the system learns users’ electricity usage habits to create a tailored energy-saving environment. Gamification is also introduced to create a reward system that stimulates users’ desire to achieve goals, thus promoting autonomous energy saving. An experiment was conducted on 62 students. In total, 59 out of 62 participants responded with a sampling error of ±2.8% at a 95% confidence level. The average system usability scale (SUS) score reached 84, surpassing the cross-industry average standard, indicating that the system is user-friendly. The average self-efficacy score for energy saving reached 4.28 (σ = 3). The system significantly impacted the participant’s motivation to enhance energy saving. The net promoter score (NPS) was 29. This indicated that, although users are generally satisfied with the system, there is still room for improvement. Full article

Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These devices enable non-invasive, real-time monitoring of biochemical, electrophysiological, and biomechanical signals, offering personalized and proactive healthcare solutions. In parallel, implantable BioMEMS have significantly enhanced long-term diagnostics, targeted drug delivery, and neurostimulation. From continuous glucose and intraocular pressure monitoring to programmable drug delivery and bioelectric implants for neuromodulation, these devices are improving precision treatment by continuous monitoring and localized therapy. This review explores the materials and technologies driving advancements in wearable and implantable BioMEMSs, focusing on their impact on chronic disease management, cardiology, respiratory care, and glaucoma treatment. We also highlight their integration with artificial intelligence (AI) and the Internet of Things (IoT), paving the way for smarter, data-driven healthcare solutions. Despite their potential, BioMEMSs face challenges such as regulatory complexities, global standardization, and societal determinants. Looking ahead, we explore emerging directions like multifunctional systems, biodegradable power sources, and next-generation point-of-care diagnostics. Collectively, these advancements position BioMEMS as pivotal enablers of future patient-centric healthcare systems. Full article

The AIoT paradigm, which combines AI with IoT, offers great advantages in manufacturing processes. However, its use in aeronautical testing is still incipient, since this kind of test must ensure strict safety requirements. This study presents one AIoT architecture aimed at structurally testing aeronautical applications that ease the integration of AI techniques to interpret the data obtained by wireless IoT devices. In addition, the authors propose implementation guidelines for developers. The presented approach was experimentally validated in the rigorous and standardized certification test of a real aerospace component, namely a flag track component of the Embraer E2 model aircraft. Recorded magnitudes with IoT devices were compared with the data obtained using conventional technologies in terms of the quality of information and compliance with the requirements of aeronautical regulations. In order to illustrate the integration of different AI techniques in the AIoT architecture, ARIMA and LSTM algorithms were used to analyze the data captured with three sensors. The obtained results proved that the AIoT architecture is valid in structural testing applications, achieving a reduction in cabling and deployment time as well as improving flexibility and scalability. The presented approach paves the way to introduce AI-based algorithms for analyzing, either in run-time and off-line, the structural testing results obtained by means of IoT devices. Full article

The integration of Internet of Things (IoT) technology into the agricultural sector enables the collection and analysis of large amounts of data, facilitating greater control over internal processes, resulting in cost reduction and improved quality of the final product. One of the main challenges in designing an IoT system is the need for interoperability among devices: different sensors collect information in non-homogeneous formats, which are often incompatible with each other. Therefore, the user of the system is forced to use different platforms and software to consult the data, making the analysis complex and cumbersome. The solution to this problem lies in the adoption of an IoT standard that standardizes the output of the data. This paper first provides an overview of the standards and protocols used in precision farming and then presents a system architecture designed to collect measurements from sensors and translate them into a standard. The standard is selected based on an analysis of the state of the art and tailored to meet the specific needs of precision agriculture. With the introduction of a connector device, the system can accommodate any number of different sensors while maintaining the output data in a uniform format. Each type of sensor is associated with a specific connector that intercepts the data intended for the database and translates it into the standard format before forwarding it to the central server. Finally, examples with real sensors are presented to illustrate the operation of the connectors and their role in an interoperable architecture, aiming to combine flexibility and ease of use with low implementation costs. Full article

The carbon footprint associated with human activity, particularly from energy-intensive industries such as iron and steel, aluminium, cement, oil and gas, and petrochemicals, contributes significantly to global warming. These industries face unique challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies, which require reliable and efficient power solutions. Conventional wireless devices powered by lithium batteries have limitations, including a reduced lifespan in high-temperature environments, incompatibility with explosive atmospheres, and high maintenance costs. This paper proposes a novel approach to address these challenges by leveraging residual heat to power IIoT devices, eliminating the need for batteries and enabling autonomous operation. Based on the Seebeck effect, thermoelectric energy harvesters transduce waste heat from industrial surfaces, such as pipes or chimneys, into sufficient electrical energy to power IoT nodes for applications like the condition monitoring and predictive maintenance of rotating machinery. The methodology presented standardises the modelling and simulation of Waste Heat Recovery Systems (IoT-WHRSs), demonstrating their feasibility through statistical analysis of IoT-WHRS architectures. Furthermore, this technology has been successfully implemented in a petroleum refinery, where it benefits from the NB-IoT standard for long-range, robust, and secure communications, ensuring reliable data transmission in harsh industrial environments. The results highlight the potential of this solution to reduce costs, improve safety, and enhance efficiency in demanding industrial applications, making it a valuable tool for the energy transition. Full article

Mitigating cyberattacks on IoT networks is critical and remains a significant challenge, as such attacks can cause severe damage to the network systems and services. Moreover, the large volume of devices in IoT networks presents another challenge in managing security to reduce the risk of attacks. The Manufacturer Usage Description (MUD) is a standard for limiting attack risks on IoT networks. However, MUD has limitations, as it relies solely on pre-defined access control list (ACL) rules to allow permitted traffic and block unknown traffic. This can lead to false-negative filtering, where malicious traffic may still be allowed by MUD, compromising an entire IoT network. This study presents the implementation of a network behaviour analysis (NBA) system for DoS/DDoS attack detection in MUD-based IoT networks. We designed a set of algorithms to enhance the effectiveness of malicious traffic detection compared to using MUD alone. The NBA system groups related traffic and detects a variety of DoS/DDoS attacks that utilise TCP and MQTT protocols. Our evaluation demonstrates that the NBA system achieves high detection accuracy, effectively identifying attacks that MUD alone would not be able to detect, thereby enhancing the effectiveness of attack detection in MUD-based IoT networks. Full article

This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Efficient waste management remains critical to achieving sustainable urban development, addressing challenges related to resource conservation, environmental preservation, and carbon emissions reduction. This review synthesizes advancements in waste management technologies, focusing on three transformative areas: optimization techniques, the integration of electric vehicles (EVs), and the adoption of smart technologies. Optimization methodologies, such as vehicle routing problems (VRPs) and dynamic scheduling, have demonstrated significant improvements in operational efficiency and emissions reduction. The integration of EVs has emerged as a sustainable alternative to traditional diesel fleets, reducing greenhouse gas emissions while addressing infrastructure and economic challenges. Additionally, the application of smart technologies, including Internet of Things (IoT), artificial intelligence (AI), and the Geographic Information System (GIS), has revolutionized waste monitoring and decision-making, enhancing the alignment of waste systems with circular economy principles. Despite these advancements, barriers such as high costs, technological complexities, and geographic disparities persist, necessitating scalable, inclusive solutions. This review highlights the need for interdisciplinary research, policy standardization, and global collaboration to overcome these challenges. The findings provide actionable insights for policymakers, municipalities, and businesses, enabling data-driven decision-making, optimized waste collection, and enhanced sustainability strategies in modern waste management systems. Full article

This paper investigates the virtualization of LoRaWAN end nodes through Linux containers (LXCs) to improve scalability, flexibility, and resource management. By leveraging lightweight Docker-based virtualization, we break down the core functions of the LoRaWAN node, comprising the application, LoRaWAN, and LoRa layers, into modular containers. In this work, a fully virtualized end node is demonstrated. The obtainable performance is not only compared against the standard approach that leverages a LoRaWAN-compliant module but also against an emulated solution that mimics the desired functionalities purely in software. A controlled, uniform testbed, exploiting the capability of a virtual machine hypervisor to change the way the underlying hardware is abstracted to guest environments, is considered. Key metrics, including resource utilization and latency, are explicitly defined and evaluated. The results underscore the potential of container technologies to transform the deployment and management of communication solutions targeting Internet-of-Things (IoT) scenarios not only for the infrastructure but also for end devices, with implications for future advancements in wireless network virtualization. Full article

The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control. Full article

The rapid growth of the Internet of Things (IoT) has significantly expanded the deployment of resource-constrained devices, introducing new security and privacy challenges. To address these concerns, the National Institute of Standards and Technology (NIST) concluded a multi-year effort by announcing ASCON as the new lightweight cryptography standard in 2023. ASCON’s cipher suite includes both Authenticated Encryption with Associated Data (AEAD) and hashing functions, ensuring authenticity, confidentiality, and broad applicability. Since its standardization, there has been a significant research effort focused on enhancing ASCON’s performance under diverse application constraints as well as assessing its vulnerability to advanced side-channel attacks. This study offers a comprehensive overview of current ASCON hardware implementations on FPGA and ASIC platforms, examining key design trade-offs. Additionally, it examines the latest side-channel attacks on ASCON were examined. These attacks exploited weaknesses in the hardware implementations rather than in the algorithm itself. Being highly efficient, they could breach both unprotected and protected implementations. This survey also reviews the proposed countermeasures against these powerful attacks and analyzes how their associated overhead conflicts with the performance demands of real-world ASCON applications. The synthesis of these findings offers clear guidelines for designers seeking to implement ASCON. At the same time, areas requiring further investigation are identified. As ASCON sees ever more widespread deployment, this review serves as a reference for understanding the current state of research and guiding future developments toward efficient and secure implementations. Full article

While the market for massive machine type communications (mMTC) is evolving at an unprecedented pace, the standardization bodies, including 3GPP, are lagging behind with standardization of truly 5G-grade cellular Internet-of-Things (CIoT) systems. As an intermediate solution, an early data transmission mechanisms encapsulating the data into the preambles has been recently proposed for 4G/5G Narrowband IoT (NB-IoT) technology. This mechanism is also expected to become a part of future CIoT systems. The aim of this paper is to propose a model for CIoT systems with and without early transmission functionality and assess the optimal distribution of resources at the random access and data transmission phases. To this end, the developed model captures both phases explicitly as well as different traffic composition in downlink and uplink directions. Our numerical results demonstrate that the use of early transmission functionality allows one to drastically decrease the delay of uplink packets by up to 20–40%, even in presence of downlink traffic sharing the same set of resources. However, it also affects the optimal share of resources allocated for random access and data transmission phases. As a result, the optimal performance of 5G mMTC technologies with or without early transmission mode can only be attained if the dynamic resource allocation is implemented. Full article

The Internet of Things (IoT) is a transformative technology that enables connection and communication between devices, systems, and objects using an existing network. This interconnectivity allows these objects to collect and analyze data, facilitating interaction in various environments. IoT spans areas such as homes, industries, cities, and even ecosystems, creating smart and responsive systems. In today’s digital age, where protecting personal, industrial, and commercial information is critical, IoT technologies must ensure a high level of security. It is essential to employ secure communication protocols or applications to ensure that IoT devices with limited resources, such as embedded systems, can communicate safely. Due to the low processing and memory capacities of these devices, traditional encryption techniques like AES are often impractical. Consequently, numerous lightweight encryption techniques have been developed specifically for use in embedded systems. The first stage of this research focused on the CLEFIA, PRESENT, and LEA algorithms outlined in the ISO/IEC 29192-2:2019 standard. The second stage involved developing a test environment to run these algorithms on embedded systems, ensuring that they were compatible with the systems’ constraints. The third stage focused on determining and analyzing the real-time energy consumption of these algorithms when implemented on embedded systems. Full article

The effectiveness of early warning systems can help people take action to mitigate the impact of extreme weather events once warnings are issued. The early warning systems developed by public agencies usually issue standard messages that, in many situations, may not affect all the people who receive the messages. In the long run, this can lead to behaviors in people who may not respond to relevant warnings, resulting in inefficiency. Users demand faster and more customized information that matches their needs, such as “How does this affect me right now?” or “What can I do to mitigate the impact?” This study proposes a decentralized framework at the community level that includes custom Internet of Things (IoT) sensors for timely information monitoring and large language models (LLMs) for the generation of user-defined warning messages. The sensors have the advantages of easy installation, low cost, and affordable maintenance fees. The trained LLMs expedite information processing given specific prompts and generate customized response messages to the users. In addition, the framework is established within a serverless environment, enabling rapid deployment and scalability. This integration of IoT sensors and LLMs demonstrates how the system performs once sensors detect flooding and how LLMs can deliver real-time, efficient, and localized action-ready information in different scenarios. This combination significantly enhances the responsiveness during flood events. Full article