Search Results (820)

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

Electricity consumption is increasing as a result of increasing people’s needs, such as lighting, heating, and comfort. Different needs come into play day by day in the houses where people live and in places used as common areas, and this increases the need for electricity. Studies have observed that almost half of the world’s electricity consumption is made by buildings. Public buildings, shopping malls, hospitals, and hotels are typical examples of such structures. However, hospitals have an important place among all building types as they contain a wide range of devices and are of critical importance to many systems. Consumption in hospitals is a necessity rather than a desire for comfort in places such as hotels and shopping malls. Therefore, analysis of the energy consumed by hospitals is one of the important things to perform to reduce the damage caused by electricity consumption to the environment. In this study, the energy analysis of a green hospital with an installed area of 55,000 square meters in Istanbul was conducted, and machine learning techniques can be used in the analysis. Among many methods used for building energy analysis, long short-term memory (LSTM) has been chosen. The available data set was analyzed with the various LSTM methods and classification and prediction operations were carried out. Error rates for each method were compared. With the results obtained, it has been observed that the vanilla LSTM method provides acceptable results in building energy analysis. 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

Rapid urbanization and the rising demand for sustainable living have encouraged the growth of smart cities, which incorporate innovative technologies to ameliorate environmental sustainability, optimize resource management, and improve living standards. The convergence of blockchain (BC) technology and the Internet of Things (IoT) presents transformative convenience for managing smart cities and achieving sustainability goals. In fact, blockchain technology combined with IoT devices provides a decentralized, transparent, and safe framework for managing massive volumes of data produced by networked sensors and systems. By guaranteeing accountability, minimizing fraud, and maximizing resource use, blockchain not only facilitates the smooth operation of smart city infrastructures but also encourages sustainable habits. The various uses of blockchain technology in smart city management and its contribution to sustainability objectives are examined in this study. Through an examination of important domains like energy distribution, waste management, transportation systems, healthcare, and governance, the research shows how blockchain promotes effective data exchange and data security, builds stakeholder trust, and makes it possible to establish decentralized organizations to improve decision-making. Full article

Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a long-short term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future. Full article

The fourth-century historian Eusebius, bishop of Caesarea, lived during the anti-Christian persecutions of the Roman emperor Maximinus Daia and believed fervently that Jesus Christ would imminently return to alleviate the suffering of God’s people. When Constantine the Great became emperor and converted to Christianity, the bishop’s disposition towards the ‘last things’ or end times, known as eschatology, suddenly changed to a belief that God’s kingdom had already been inaugurated in this emperor’s reign. In this way, Eusebius conflated Church and Empire into a single organism governed by the emperor on behalf of Christ. This article demonstrates that this disposition in fact emerged from the bishop’s problematic Christology. Heretofore, the concept of the Logos had been applied to Son of God as creator of the world and who assumed human nature as Jesus. However, Eusebius’ disposition towards the Logos was subordinationist and dissociative, thus paving the way for him to depict the emperor as an agent of, and inhabited by, the Logos in the eschatological working out of earthly affairs from the vantage point of the city of Rome. Eusebius therefore essentially replaced Christ’s eschatological agency in the usurpation of the Church by the eternal city that was ultimately recapitulated within Constantine himself, even after the latter had died. Full article

Culture encompasses, among other things, the ways of living, feeling, and thinking of a social group and is transmitted from one generation to the next. In part, this transmission is accomplished by older adults. Generative cultural demand refers to the perception that older adults have of what society and their families expect them to contribute to the benefit of younger generations and the communities in which they live. This phenomenological study explores generative cultural demand. Semi-structured interviews were conducted with 20 older adults between the ages of 61 and 89 from urban and rural areas in Mexico. The interviews were transcribed and analyzed through content analysis using the MAXQDA program. The results show that the main category of cultural demand was wisdom, conceived as the transmission of experiences, knowledge, behavior, traditions, and values, which varied by sex and by type of urban or rural area. In rural areas, women emphasized the transmission of experiences about daily life, while men focused on matters of labor; in urban areas, both men and women highlighted the transmission of personal experiences. Participants considered themselves to be appreciated, still capable of contributing to society, and as a reservoir of knowledge, traditions, values, and principles useful to future generations. Full article

Due to the increased age of the global population, the proportion of the elderly population continues to rise. The safety of the elderly living alone is becoming an increasingly prominent area of concern. They often miss timely treatment due to undetected falls or illnesses, which pose risks to their lives. In order to address this challenge, the technology of indoor abnormal behavior detection has become a research hotspot. This paper systematically reviews detection methods based on sensors, video, infrared, WIFI, radar, depth, and multimodal fusion. It analyzes the technical principles, advantages, and limitations of various methods. This paper further explores the characteristics of relevant datasets and their applicable scenarios and summarizes the challenges facing current research, including multimodal data scarcity, risk of privacy leakage, insufficient adaptability of complex environments, and human adoption of wearable devices. Finally, this paper proposes future research directions, such as combining generative models, federated learning to protect privacy, multi-sensor fusion for robustness, and abnormal behavior detection on the Internet of Things environment. This paper aims to provide a systematic reference for academic research and practical application in the field of indoor abnormal behavior detection. Full article

The development of students’ skills, abilities, competences and knowledge is the basis for sustainable socio-economic development. Today we live in a world that is in continuous change, both economically and socially, which also determines a change in the requirements on the labor market and therefore graduates and higher education institutions must continuously adapt to these changes. Thus, higher education institutions must adapt their teaching strategies and educational offer, while students must develop new skills and competences. The main purpose of this article is to analyze the extent to which the information, skills, attitudes and competences acquired by graduates of physical education and sports faculties during their years of study influence their standard of living, job satisfaction and confidence. To achieve this objective, we asked the following research questions: 1. To what extent do the information, skills, abilities and competences acquired by students during their years of study influence their income level, standard of living, job satisfaction and level of confidence in the workplace? 2. What is the self-perception of students regarding the information, skills, abilities and knowledge that students possess? 3. What is the perception of employers regarding the information, skills, abilities and knowledge that students possess? 4. To what extent are there differences between students’ self-perception and employers’ perception regarding the information, skills, abilities and knowledge that students possess? Thus, data were collected through two questionnaires, one distributed among 333 graduates from physical education and sports faculties in Romania and one to 11 employers working in the sports industry in Romania. The data obtained from the students were analyzed using SPSS 24, and it was found that there is a small correlation between the information, skills, competences and knowledge acquired during the years of study and the standard of living, job satisfaction and the confidence in one’s own ability to successfully perform tasks at work. Among the skills, abilities and aptitudes that students consider themselves to excel in are a passion for sports, the continuous desire for improvement, conscientiousness, teamwork, openness to new things and respect for hierarchies and regulations. At the opposite end, graduates consider that they need to improve their public speaking skills, management skills, their ability to communicate in a foreign language, their ability to sell themselves and their ability to manage a project. Full article

An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications. Full article

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air quality, structural integrity, and environmental changes. These systems provide early warnings for natural disasters such as earthquakes, floods, and wildfires, enabling authorities to respond proactively. In emergency management, IoT devices help coordinate resources and improve situational awareness during crises. Real-time data from wearable devices, smart infrastructure, and communication systems allow responders to track people, manage evacuations, and deploy resources more effectively. For example, IoT-enabled drones and autonomous vehicles are used to deliver supplies or assess damage in hazardous areas without risking human lives. IoT technologies improve post-disaster recovery by continuously monitoring areas for safety hazards and supporting infrastructure restoration. Smart traffic management systems assist in controlling traffic flow for emergency vehicles, while IoT-based communication networks ensure connectivity when traditional systems fail. The IoT significantly enhances the speed, accuracy, and effectiveness of disaster response and public safety operations, leading to the better protection of communities and faster recovery from emergencies. Full article

The increased use of electronic devices in the Internet of Things (IoT) leads not only to an improved comfort of living but also to an increased risk of attacks. IoT security has thus become an important research field. However, due to limits on performance and bandwidth, IoT devices are often not powerful enough to execute, e.g., costly cryptographic algorithms or protocols. This limitation can be solved through a delegation concept. By delegating certain operations to devices with sufficient resources, it is possible to achieve a high level of security without overloading a device that needs protection. In this paper, we give an overview of current approaches for security delegation in the context of IoT, formalise security notions, discuss the security of existing approaches, and identify further research questions. Furthermore, a mathematical formalisation of the CIA triad (confidentiality, integrity, and availability) is proposed for the predefined application areas, in order to evaluate the different approaches. Full article