Internet Of Things Architecture Research Articles

AI-Driven IoT Healthcare System for Real-Time ECG Analysis and Comprehensive Patient Monitoring

This study explores the integration of deep learning and Internet of Things (IoT) technologies to enhance healthcare delivery, with a primary focus on improving electrocardiogram (ECG) analysis and real-time patient monitoring systems. The research presents the development of two innovative deep learning models based on the MIT-BIH dataset, enabling highly accurate ECG analysis. One model is trained for precise R-R peak detection, while the other performs effective classification of ECG signals into five distinct disease categories. The study also introduces an integrated healthcare system that seamlessly captures patients' real-time physiological data, including ECG, SpO2, and temperature, using an ESP32 microcontroller and Raspberry Pi. An IoT infrastructure with Node-RED IBM Platform and Message Queuing Telemetry Transport (MQTT) securely transmits the ECG data to the advanced analysis algorithms. The user interface displays patients' vital signs, including heart rate, oxygen saturation, and temperature, providing healthcare professionals with comprehensive real-time insights. By integrating the deep learning models, which achieve approximately 99% accuracy, alongside robust sensor technology and an IoT architecture, this system aims to transform healthcare by enabling highly precise ECG analysis and remote patient monitoring. The findings of this study underscore the potential of the synergistic convergence of deep learning, sensor technology, and IoT to advance healthcare delivery and improve patient outcomes.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations.

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

A Review of Physical and Logical Design in IoT: Comparative Analysis of Devices, Protocols, Communication Models, and APIs

The Internet of Things (IoT) has emerged as a transformative technology, enabling connectivity and data exchange among a vast network of devices. This paper presents a comprehensive comparative analysis of the physical and logical design elements of IoT systems, focusing on IoT devices, communication protocols, models, and APIs (REST and WebSocket). The research highlights the critical role that both physical and logical components play in ensuring efficient, secure, and scalable IoT deployments. By examining the integration of these aspects, the paper aims to provide insights for optimizing IoT architectures for various use cases, particularly in resource-constrained environments.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

A centralized frost detection and estimation scheme for Internet-connected domestic refrigerators

Frost accumulation on heat exchange units is a significant problem in refrigeration systems, adversely affecting their operating performance and thereby leading to increased power consumption. Therefore, timely detection and accurate quantification of frost are crucial for effective defrosting strategies. This study presents a novel centralized cloud-based IoT scheme for frost detection and thickness estimation. The image processing is performed on the cloud server to process evaporator coil images for frost thickness quantification. Experiments were conducted on a domestic refrigerator to assess the effectiveness of the proposed image-processing approach and determine latency and processing time. The presented scheme effectively quantifies frost thickness on the evaporator in the 1–5 mm range with a 10.8% error margin. The total inference time, which includes image acquisition, pre-processing, transmission latency, and frost thickness estimation, is approximately 5.15 seconds. The results demonstrate that the proposed image processing method performs comparably to conventional sensors and similar image processing techniques. Moreover, the centralized cloud-based IoT architecture presented effectively meets the scalability demands of consumer refrigerators.

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.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

Cognitive Transformation in Personal IoT: Pioneering Intelligent Automation

ABSTRACT In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience.

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

A Low-Power IoT Architecture for Real-Time Detection and Notification of Unauthorized Tree Poaching

A Low-Power IoT Architecture for Real-Time Detection and Notification of Unauthorized Tree Poaching

Study to integrate Delay-Tolerant Network protocols in IoT LEO constellations for flood prevention

In the era of the Internet of Things (IoT) the development of Direct-to-Satellite IoT (DtS-IoT) applications are becoming increasingly relevant. These applications are based on enabling IoT devices to communicate directly with satellites. In this scenario, LEO satellites can provide global IoT service coverage, becoming essential to connect devices in remote areas. As an example, the deployment of NarrowBand-IoT (NB-IoT) service is being actively investigated with the apparition of Non-Terrestrial Network (NTN) and DtS-IoT concepts. Although some initiatives propose seamless and ubiquitous service, these features may not be required for IoT applications. This relaxes the requirements in the satellite constellation architecture, enabling it to be more sparse. The connection between sensors, satellites and ground segment becomes intermittent and discontinuous in these non-dense architectures. Delay/Disruptive-tolerant protocols are essential to coexist with this network characteristics. Unfortunately, traditional IoT protocols have not been designed with this delay-tolerant feature. This work tackles this challenge by integrating Delay-Tolerant Network protocols with NB-IoT architecture. Specifically, the integration is based on interconnecting the Bundle Protocol and IP traffic through a novel interface. This development has been divided in three phases: (1) The definition of an architecture and protocol stack to tackle NTN IoT scenarios with discontinuities, (2) the analysis from simulated data of the resulting protocol stack in a realistic scenario based on flooding prevention, and (3) the implementation and validation of it in a laboratory testbed. The proposed case study uses a Direct-to-Satellite IoT architecture to create an early warning flooding detection system. The simulation results provide insights of the achieved performance with these architectures when servicing hundreds of sensing nodes.

Leveraging IoT for Enhanced Supply Chain Management in Manufacturing

Information Technology especially through use of the Internet of Things (IoT) bring significant change to the manufacturing supply chain in terms of efficiency, visibility and responsiveness. This paper aims to assess the position of IoT with reference to contemporary manufacture; with regard to its advantages and the issues with its implementation. This paper aims to review IoT applications which foster supply chain management improvement through the identification of real time tracking, predictive maintenance, and data analysis. Furthermore, the paper discusses the technical, organizational, and security issues are described that manufacturers encounter while implementing IoT with the current systems. Introducing future trends of the IoT and its implication for supply chain management, the prospects for evolution are described and envisioned. This work supports the centrality of IoT architectures vital for manufacturers in today’s globalized market where unpredictability breaches financial plans. Lastly, the implemented research findings on how IoT can be effectively incorporated in SCM are discussed followed by a proposal on future research.

Implementation of an IoT architecture for promoting healthy air quality in 84 homes of families with children

IoT systems incorporating low-cost air quality sensors (LCS) have emerged as valuable tools for empowering citizens to take action to improve indoor air quality (IAQ). This work aimed to assess the effectiveness of a newly developed IoT system in promoting behavioural changes and improving IAQ in 84 homes of families with children. To accomplish this aim, a modular IoT architecture using calibrated LCS was developed to collect real-time data on CO2, temperature, relative humidity and particulate matter (PM2.5 and PM10) during a randomised cross-over trial (November 16, 2022–January 24, 2023). The intervention under study consisted of providing access to real-time IAQ data to participants and alerting them via a smartphone app when CO2, PM2.5, and/or PM10 concentrations were higher than the recommended limits. The findings showed that LCS readings were strongly correlated with those obtained from reference equipment. Nevertheless, long-term assessments revealed a signal degradation effect for the readings obtained from the LCS PM sensor. The comparison of IAQ data from control and intervention periods showed a significant alleviation of CO2 concentrations (mean reduction of 10.3 % in 52 out of 84 participant homes). Notably, about 70 % of participants recognized that data presented through the app motivated them to take valuable actions to enhance IAQ. Overall, this study constitutes a step forward to provide valuable field evidence on the strengths and limitations of using IoT systems based on LCS to empower citizens on the factors that may influence exposure to air pollution at home.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Attacks, Challenges, and Countermeasures for an Integrated IoT Framework

The Internet of Things (IoT) has lately attracted a lot of interest owing to the fact that it has several applications in a variety of fields and makes communication easier across a variety of levels. The IoT is made up of three unique levels, which are the physical layer, the network layer, and the application layer at the most fundamental level. The purpose of this study is to examine security threats and the responses that correspond to them for each layer of the IoT architecture. Additionally, the article investigates the implications that arise from security breaches on IoT devices. In addition to providing a detailed taxonomy of attacks, this research reveals security weaknesses that are present inside each tier of the IoT network. In addition to this, the article investigates a variety of modern security frameworks, investigates probable security flaws, and investigates remedies that correspond to those vulnerabilities. In conclusion, the article proposed the "Unified Federated Security Framework," which is an all-encompassing security architecture made specifically for IoT networks. In order to facilitate the ability of users inside the security layer to acquire access to resources situated within a separate security layer, the proposed framework is based on the building of trust across the three levels. This allows users to gain access to resources without having to utilise the account of another user.

IoT Systems Based Microgrid: A Review

Smart microgrids, as the foundations of the future smart grid, combine distinct Internet of Things (IoT) designs and technologies for applications that are designed to create, regulate, monitor, and protect the microgrid (MG), particularly as the IoT develops and evolves on a daily basis. A smart MG is a small grid that may operate individually or in tandem with the electric grid, and it is ideal for institutional, commercial, and industrial consumers, as well as urban and rural societies. A MG can operate in two methods (stand-alone and grid-connected), with the ability to transition between modes due to local grid faults, planned maintenance, expansions, deficits and failures in the host system, and other factors. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optimal energy management, security protocols, control methods, and applications in the MG, with numerous distributed energy resources (DER) and interconnected loads. The use of IoT architecture for MG operations and controls is discussed in this research. With the use of power grid equipment and IoT-enabled technology

A revocable multi-authority attribute-based encryption scheme for fog-enabled IoT

As more applications move data storage to the cloud, protecting sensitive data becomes increasingly important, especially for the Internet of Things (IoT) environments. Ciphertext-policy attribute-based encryption (CP-ABE) is a practical approach for confidentiality and secure access control for data outsourced to the cloud. However, the underlying CP-ABE operations based on bilinear pairings are too demanding for resource-constrained IoT devices. Furthermore, applications such as Industrial IoT (IIoT) have requirements (efficiency, operative, and security) that existing CP-ABE proposals cannot fulfill, and advanced IoT architectures (e.g., fog computing) have not been well-exploited. This paper proposes a novel CP-ABE scheme suitable for IoT scenarios, using an IIoT generic model as a reference. It targets multiple attribute authorities, outsourced encryption and decryption to fog nodes, user revocation, and asymmetric pairings constructions to achieve recommended security levels. As the main distinctive, revocation is defined using a broadcast encryption-based approach, allowing data owners to enforce user revocation over their outsourced data. According to the performance analysis, the proposed scheme achieves high efficiency for IoT nodes. It is also competitive in terms of storage, bandwidth, and computation efficiency compared to previous proposals. Moreover, the security of the suggested construction is demonstrated against chosen-plaintext attacks.

The applications of the internet of things in smart cities governance: a bibliometric study

Integrating the Internet of Things (IoT) in smart cities can solve rapid urbanization. It will provide better urban services, addressing urban problems that develop because of enhancing urbanization without establishing policies focusing on well-being. The present investigation explores the growing trend of the dynamic publications associated with the advanced technologies integrated into smart city publication productivity using bibliometric analysis. This approach enables the detection of the usage history, the usage scheme, and the research landscape topic in scientific papers on IoT in smart cities. This study included 976 articles from 2013 to 2023. The area of research in smart cities is categorized into six important clusters: (1) IoT application areas in smart cities, (2) IoT infrastructure for smart cities, (3) IoT security and privacy for smart cities, (4) IoT architecture for smart cities, (5) IoT technologies and communication protocol for smart cities, (6) IoT data analysis for smart cities. Moreover, this study reveals that the issues related to new technologies, security, and privacy of IoT to integrate into smart cities would be an interesting topic for future research.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments