Internet Of Things Research Articles

Request Deadline Split and Interference‐Aware Request Migration in Edge Cloud

ABSTRACTEdge computing extends computing resources from the data center to the edge of the network to better handle latency‐sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency‐sensitive tasks, a feasible solution is to offload some latency‐sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub‐problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi‐resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one‐third the number of request violations of other methods.

Enhancing IoT Security through an Artificial Neural Network Approach

This study aims to fortify Internet of Things (IoT) security through the strategic implementation of Artificial Neural Networks (ANNs). With the rapid expansion of IoT devices, traditional security measures have struggled to cope with the dynamic and complex nature of these environments. ANNs, known for their adaptability, are explored as a promising solution to enhance security. The central objective is to significantly improve the accuracy of IoT security measures by optimizing ANN architectures. Using a curated dataset with key environmental parameters, the study evaluates three ANN models—Backpropagation Neural Network (BPNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). The evaluation metrics include accuracy, precision, recall, and F1-score across different train-test splits. Results show that LSTM consistently outperforms BPNN and MLP, demonstrating superior accuracy and the ability to capture temporal dependencies within IoT security data. Implications stress the importance of aligning model selection with specific application goals, considering factors like computational efficiency. In conclusion, this research contributes valuable insights into the practical implementation of ANNs for IoT security, guiding future optimization efforts and addressing real-world deployment challenges to safeguard sensitive data and ensure system resilience in the evolving IoT landscape.

Health IoT Threats: Survey of Risks and Vulnerabilities

The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the healthcare sector over time. We collected 279 studies from 5 scientific databases, of which 69 articles met the requirements for inclusion. We performed thematic and qualitative content analysis to extract trends and information. According to our analysis, the vulnerabilities in IoT in healthcare are classified into three main layers: perception, network, and application. We comprehensively reviewed IoT privacy and security threats on each layer. Different technological advancements were suggested to address the identified vulnerabilities in healthcare. This review has practical implications, emphasizing that healthcare organizations, software developers, and device manufacturers must prioritize healthcare IoT security and privacy. A comprehensive, multilayered security approach, security-by-design principles, and training for staff and end-users must be adopted. Regulators and policy makers must also establish and enforce standards and regulations that promote the security and privacy of healthcare IoT. Overall, this study underscores the importance of ensuring the security and privacy of healthcare IoT, with stakeholders’ coordinated efforts to address the complex and evolving security and privacy threats in this field. This can enhance healthcare IoT trust and reliability, reduce the risks of security and privacy issues and attacks, and ultimately improve healthcare delivery quality and safety.

Automatic Indoor Thermal Comfort Monitoring Based on BIM and IoT Technology

Building Information Modeling (BIM) and Internet of Thing (IoT) integration technologies can improve operational efficiency in the operational phase of construction projects. Currently, research on the integration of BIM and IoT has yet to ensure secure data transmission and lacks real-time data processing capabilities. This study builds a framework to collect and analyze BIM and IoT data in real time. The framework is verified to be effective through a case study in an office building. The monitoring system can automatically calculate the Predicted Mean Vote (PMV) value, upload and update real-time temperature and humidity data, and visualize thermal comfort through heat maps. The proposed integration approach offers building management strategies to enhance thermal comfort in office environments, fostering a more inclusive and accommodating workspace that acknowledges the diverse cultural backgrounds of occupants.

IoT and Enterprise Architecture Synergy: Driving Industrial Process Optimization

The Internet of Things has emerged as a transformative force, revolutionizing industries by interconnecting devices, data, and processes. In other words, the rapid advancement of the Internet of Things has redefined how industries operate, facilitating the seamless integration of devices, data, and processes. On the other hand, the issue of enterprise architecture is also discussed, which is a comprehensive and integrated approach in line with the organization's strategic planning and macro business policies. This article delves into and focuses on how the Internet of Things and Enterprise Architecture impact industries, and explores the opportunities and challenges presented by this influence, with a specific focus on the Industrial Internet of Things.

Condition Monitoring of Electrical Transformers Using the Internet of Things: A Systematic Literature Review

The adoption of Internet of Things (IoT) technology for transformer condition monitoring is increasingly replacing traditional methods. This systematic review aims to evaluate the existing research on IoT frameworks used in transformer condition monitoring, providing insights into their effectiveness and research trends. This review seeks to identify the leading IoT frameworks employed in transformer condition monitoring; analyze the key research objectives, methods, and outcomes; and assess the global research distribution and technological tools used in this field. A systematic literature review was conducted by searching published databases using keywords related to “Internet of Things”, “transformers”, “condition monitoring”, and “fault diagnosis”. The search spanned publications released between 2014 and 2024, yielding 262 articles. Of these, 120 met the predefined review criteria and were included for further analysis. This review found that Arduino boards are the most used microcontrollers for monitoring and analyzing transformer operational parameters, with Arduino IDE 1.8 being the predominant software for programming. The primary research focus in the reviewed literature is the identification of transformer faults. The geographical distribution of research contributions shows that India leads with 65% of the studies, followed by China (11%) and Pakistan (5%). The findings indicate a strong global interest in developing IoT-based transformer condition monitoring systems, particularly in India. This review highlights the potential of IoT technologies to enhance transformer monitoring and diagnostics. The insights gained from this review can guide future research and the development of more advanced IoT frameworks for transformer condition monitoring.

Implementation of the Telemetric Integration of the BIM-RFID in Context of Access Control

Building Information Modelling (BIM) integration with the Internet of Things (IoT) is progressing. The high level of BIM maturity involves using sensor data to manage processes or objects. The article presents the process of creating a telemetry connection between the BIM model and a Radio-Frequency Identification (RFID) sensor in the context of gaining access to various parts of a building. The process of creating a connection using an experimental set based on a microcontroller board for RFID reader support is described. The set was programmed using multiple programming languages and artificial intelligence. The article presents a unique process of connecting an RFID reader with BIM using a simple model that can be replicated in other contexts (e.g., gaining access to different parts of a construction site). Previous research shows that the unidirectional connection of IoT sensors with BIM models is not difficult. Instead, real-time bidirectional and stable connection (telemetry) is problematic. The authors undertook to fill this research gap using a proprietary IoT kit, programming sequence, and lightweight communication protocol. The paper makes a significant contribution to the discussion and understanding of BIM-IoT technology integration. The article also includes the limitations and possibilities for further development of such a connection.

Healthcare Internet of Things system implementations for COVID-19 prevention

BackgroundIn response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services.Methods and FindingsTo address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children’s hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected.ConclusionIn conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation.

Self-powered wireless sensor system utilizing a thermoelectric generator for photovoltaic module monitoring application

In this work, we demonstrate a self-powered wireless PV module monitoring system that utilizes a thermoelectric generator (TEG) to convert residual thermal energy from the PV module into electrical power. We investigated the TEG performance with and without the heat sink. Results show that the temperature difference between the hot and cold sides of the TEG increased to 7.2 °C with the heat sink, compared to only 1 °C without it, at a hot side temperature of 50 °C. We integrated the TEG/heat sink with the PV module, which served as the heat source, achieving a maximum output power of 0.981 mW at a voltage of 0.06 V under a temperature gradient of 3.6 °C in a 1 sun condition. We successfully demonstrated a self-powered wireless PV monitoring sensor system by integrating a step-up voltage converter, microcontroller, IR thermometer, Bluetooth communication module, and the TEG/heat sink, which generated sufficient power for the monitoring system operation. The findings introduce a novel solution for wireless PV module monitoring that operates independently of grid connections or battery power. This innovation not only signifies advancements in renewable energy management but also opens new opportunities in the Internet of Things (IoT) sector.

The Evolution of Edge Computing in a Data-Driven World

Edge computing is revolutionizing data processing and storage by bringing computational and data storage facilities closer to the data sources. This technique tackles the challenges faced by conventional clouds such as high latency and network congestion by employing the concept of data processed at the edge of the network in real-time. This paper discusses the edge computing paradigm and its evolution by covering the relevant concepts, benefits and trends that are changing within various industries. In particular, we assess the factors that make edge computing beneficial in terms of security, performance and efficiency concerns in smart cities, self-driving cars and internet of things (IoT) industries. This paper also reviews and reflects on the challenges of edge computing in terms of security, interoperability and scalability issues and how it can be overcome by integrating edge computing with fog computing, 5G and artificial intelligence (AI).

Digital Economy Oriented Tourism Industry Data Analysis in Semantic IoT

ABSTRACTThe digital economy has revolutionized the tourism industry by integrating Semantic Internet of Things (IoT) technologies for enhanced data analysis. This paper proposes a comprehensive method for analyzing tourism flow using Semantic IoT. The method combines semantic data processing with advanced machine learning techniques, specifically stacked autoencoders and long short‐term memory (LSTM) models, to understand and predict changes in passenger flow. The approach addresses challenges of high dimensionality and complex temporal characteristics in tourism data, offering significant support for tourism management, improving operational efficiency, and enhancing the visitor experience. Experimental results demonstrate the effectiveness of the proposed method in analyzing and predicting tourism passenger flow data, providing accurate and insightful trends that are crucial for decision‐making in the tourism industry.

Horti-IoT: a new data framework for Dutch horticulture

Abstract Consistency issues limit the sharing of horticultural data across multiple systems, resulting in challenges for users to analyze data effectively across various systems utilizing artificial intelligence technology. Introducing data governance principles can help standardize and unify data practices, making it easier for analysts to locate, comprehend, transfer and integrate data from diverse sources to enable data-driven horticulture. Implementing data governance and principles specific to horticulture can assist in standardizing the layout and format of data structures from different sources. This study aims to propose a new governance framework, Horti-IoT, based on the Data Management Body of Knowledge and several structured frameworks for the Internet of Things (IoT) governance that will lead to data-driven horticulture. This study is empirical in nature. The Dutch horticulture stakeholders are involved in this initiative, providing the data, knowledge, and experiences needed for this study. The data stream from various sources, including camera images, sap flow sensors, climate sensors and manually measured growth data. The key findings following the implementation of the Horti-IoT framework’s principles are reduced workload for data analysts, efficiency in plant monitoring, savings time in pre-processing, enhanced water resource management, reduced system administrator contacts and compliance with General Data Privacy Regulation. The new proposed Horti-IoT framework, compatible with Dutch horticulture, is presented. The data were obtained from the Lab greenhouse at the World Horti Centre in the Netherlands, in the framework of the Regionale SIA RAAK MKB call March 2022-September 2024 subsidy funds for project title ‘Gewasgroei Goed Gemeten (GeGoGe). This project is a collaboration between three educational institutions. Inholland University of Applied Science, the Hague University of Applied Science, Lentiz Vocational School, and stakeholders.

Research on the High-Quality Development of Agricultural Supply Chain Financial Ecosystem Empowered by Science and Technology

A systematic perspective on agricultural supply chain finance can offer fresh insights into its development. The high-quality development of the agricultural supply chain finance ecosystem is crucial for the comprehensive revitalization of rural areas and the realization of agricultural power. Based on the current state of development of this ecosystem, this paper identifies several bottlenecks, such as insufficient policy and technical support in the macro-environment system, weak incentives for independent circulation within the industrial environment system, and inadequate motivation for stakeholders to participate in the micro-environment system. To address these issues, this paper proposes that the visualization, digitalization, and authenticity characteristics of the “blockchain + Internet of Things” technology architecture can effectively resolve these bottlenecks. Additionally, targeted strategies are suggested to promote the high-quality development of the agricultural supply chain finance ecosystem.

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Emerging non-volatile memory (NVM) technologies based nano-oscillators: Materials to applications

This comprehensive study provides a detailed review toward ongoing research on emerging non-volatile memory technologies based nano-oscillators, i.e., from the perspective of materials to applications. Depending on the materials used to fabricate them, the whole class of emerging nano-oscillators has been broadly classified into two categories: (i) electronic and (ii) spintronic oscillators. Moreover, various material-compositions explored for fabricating the oscillatory devices, their physical properties responsible for generating the oscillations, and device architectures are thoroughly reviewed. Furthermore, various advanced computing applications (i.e., realized through these oscillatory devices), such as Internet of Things, neuro-inspired computing, and sensing, are also studied and their key attributes are highlighted.

Water-Triboelectrification-Complemented Moisture Electric Generator.

Energy harvesting from ubiquitous natural water vapor based on moisture electric generator (MEG) technology holds great potential to power portable electronics, the Internet of Things, and wireless transmission. However, most devices still encounter challenges of low output, and a single MEG complemented with another form of energy harvesting for achieving high power has seldom been demonstrated. Herein, we report a flexible and efficient hybrid generator capable of harvesting moisture and tribo energies simultaneously, both from the source of water droplets. The moisture electric and triboelectric layers are based on a water-absorbent citric acid (CA)-mediated polyglutamic acid (PGA) hydrogel and porous electret expanded polytetrafluoroethylene (E-PTFE), respectively. Such a waterproof E-PTFE film not only enables efficient triboelectrification with water droplets' contact but also facilitates water vapor to be transferred into the hydrogel layer for moisture electricity generation. A single hybrid generator under water droplets' impact delivers a DC voltage of 0.55 V and a peak current density of 120 μA cm-2 from the MEG, together with a simultaneous AC output voltage of 300 V and a current of 400 μA from the complementary water-based triboelectric generator (TEG) side. Such a hybrid generator can work even under harsh wild environments with 5 °C cold and saltwater impacts. Significantly, an optical alarm and wireless communication system for wild, complex, and emergency scenarios is demonstrated with power from the hybrid generators. This work expands the applications of water-based electricity generation technologies and provides insight into harvesting multiple potential energies in the natural environment with high output.

Hybridization of Learning Techniques and Quantum Mechanism for IIoT Security: Applications, Challenges, and Prospects

This article describes our point of view regarding the security capabilities of classical learning algorithms (CLAs) and quantum mechanisms (QM) in the industrial Internet of Things (IIoT) ecosystem. The heterogeneity of the IIoT ecosystem and the inevitability of the security paradigm necessitate a systematic review of the contributions of the research community toward IIoT security (IIoTsec). Thus, we obtained relevant contributions from five digital repositories between the period of 2015 and 2024 inclusively, in line with the established systematic literature review procedure. In the main part, we analyze a variety of security loopholes in the IIoT and categorize them into two categories—architectural design and multifaceted connectivity. Then, we discuss security-deploying technologies, CLAs, blockchain, and QM, owing to their contributions to IIoTsec and the security challenges of the main loopholes. We also describe how quantum-inclined attacks are computationally challenging to CLAs, for which QM is very promising. In addition, we present available IIoT-centric datasets and encourage researchers in the IIoT niche to validate the models using the industrial-featured datasets for better accuracy, prediction, and decision-making. In addition, we show how hybrid quantum-classical learning could leverage optimal IIoTsec when deployed. We conclude with the possible limitations, challenges, and prospects of the deployment.

Overview of Cyber Attacks Classification and Detection in IoT using CNN-Deep Reinforcement Learning

Millions of digital devices total the Internet of Things (IoT), and this allows very easy interaction from users connecting the devices. IoT is one of the tech sectors that is expanding most rapidly, but it can also be very vulnerable to hazards. Infections and abnormal placement on the Internet of Things (IoT) framework is an increasing threat in the field of technology. In view of the growing IoT foundation usage across all industries, attacks and dangers on these systems have also grown proportional. Leveraging typical machine learning methods, cyber-attack detection plays a critical role in avoiding damage from cyberattacks on IoT devices. IoT Cyber Attacks are Not Detected by ANN Artificial Neural Networks Using Deep Learning Techniques CNN-DRL (Convolutional Neural Networks-Deep Reinforcement Learning) Hybrid Approach: Detects Attacks, including Distributed Denial of Service (DDoS), Zero-day, and Eavesdropping Attacks.

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.