Internet Of Things Architecture Research Articles

On the impact of Industrial Internet of Things (IIoT) - mining sector perspectives

ABSTRACT The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry.

Velocious: A Resilient IoT Architecture for 6G Based Intelligent Transportation System with Expeditious Movement Mechanism

Velocious: A Resilient IoT Architecture for 6G Based Intelligent Transportation System with Expeditious Movement Mechanism

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment.

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

Blockchain-Enhanced Sensor-as-a-Service (SEaaS) in IoT: Leveraging Blockchain for Efficient and Secure Sensing Data Transactions

As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.

Real-time IoT architecture for water management in smart cities

This paper presents a digital system that aims to analyze real-time data obtained from sensors installed in a city's water distribution infrastructure. The system’s primary objective is to monitor water quality and generate alerts when necessary. The following water quality metrics are used: Flow, pH, Turbidity, Free Chlorine, Nitrate, and Fluoride. The data gathered from sensors is initially processed by a distributed system, which generates multiple visualizations that synthesize large amounts of information. These visualizations facilitate real-time monitoring of the sensor's status. Additionally, citizens can receive updates on any possible issues in the water distribution network through WhatsApp messages. By addressing the limitations of traditional water quality monitoring methods, this system contributes to a noteworthy enhancement in public water supply services. Consequently, it improves the overall quality of life for the citizens.

Incorporating the Internet of Things (IoT) Learning Module into the Smart Building Course

The utilization of Internet of Things (IoT) devices is increasing very rapidly, creating a demand for skilled professionals who can navigate the complexities of this technology. It is a crucial educational challenge to prepare undergraduates to become contributors in this emerging field. The curriculum needs to be updated to offer students both theoretical learning and practical application so they can benefit from it. This paper introduces a comprehensive IoT learning module to the final-year students of civil engineering in an elective course, Smart Building. The proposed learning module includes an introduction to IoT concepts and applications, IoT devices, communication protocols, cloud platforms, user interfaces, and IoT risk management and security, all of which can be easily integrated into existing courses. Results from the module are presented using direct and indirect assessments, including assignments, hands-on practical, examinations, and surveys. These assessments are designed to evaluate the students’ understanding and the challenges they encountered in their IoT learning process. The findings indicate that a significant majority, surpassing 90% of the students, have demonstrated understanding of a minimum of three essential components of the fundamental IoT architecture, despite their limited background knowledge at the beginning of the course. Additionally, this learning module can serve as a valuable resource for other educators who intend to deliver IoT-related courses.

Exercise Promotion System for Single Households Based on Agent-Oriented IoT Architecture.

People living alone encounter well-being challenges due to unnoticed personal situations. Thus, it is essential to monitor their activities and encourage them to adopt healthy lifestyle habits without imposing a mental burden, aiming to enhance their overall well-being. To realize such a support system, its components should be simple and loosely coupled to handle various internet of things (IoT)-based smart home applications. In this study, we propose an exercise promotion system for individuals living alone to encourage them to adopt good lifestyle habits. The system comprises autonomous IoT devices as agents and is realized using an agent-oriented IoT architecture. It estimates user activity via sensors and offers exercise advice based on recognized conditions, surroundings, and preferences. The proposed system accepts user feedback to improve status estimation accuracy and offers better advice. The proposed system was evaluated from three perspectives through experiments with subjects. Initially, we demonstrated the system's operation through agent cooperation. Then, we showed it adapts to user preferences within two weeks. Third, the users expressed satisfaction with the detection accuracy regarding their stay-at-home status and the relevance of the advice provided. They were also motivated to engage in exercise based on a subjective evaluation, as indicated by preliminary results.

IoT forensics in ambient intelligence environments: Legal issues, research challenges and future directions

Due to the abundance of the Internet of Things (IoT), smart devices are widely utilized which helps to manage human surroundings and senses inside and outside environments. The huge amount of data generated from the IoT device attracts cyber-criminals in order to gain information from the significant relationship between people and smart devices. Cyber-attacks on IoT pose a severe challenge for forensic experts. Researchers have invented many techniques to solve IoT forensic challenges and to have an in-depth knowledge of all the facts internal as-well-as external architecture of IoT needs to be understood. In this paper, an attempt has been made to understand the relationship between security and forensics incorporating its strengths and weaknesses, which has not been explored till date to the best of our knowledge. An attempt has also been made to classify literature into three categories: physical level, network level, and cloud level. These include evidence sources, areas of IoT forensics, potential forensic information, evidence extraction techniques, investigation procedures, and legal issues. Also, some prominent IoT forensic use cases have been recited along with providing the key requirements for forensic investigation. Finally, possible research problems in IoT forensic have been identified.

Enhancing Trust and Security in IoT Architecture for Low-Cost Microcontroller Devices using Elliptic Curve Cryptography

Abstract—In the rapidly expanding universe of the Internet of Things (IoT), low-cost microcontroller devices, such as the NodeMCU, have become ubiquitous. While these devices have transformed how we interact with the physical world, they also introduce significant security vulnerabilities. Chief among these is susceptibility to man-in-the-middle (MITM) attacks, which pose a serious risk to the confidentiality and integrity of data transmitted across these networks. This study addresses this critical issue by proposing the adoption of Elliptic Curve Cryptography (ECC), a cryptographic method known for its efficiency and strong security, despite the computational limitations of low-cost IoT devices. We conducted an experiment demonstrating the ease with which an MITM attack can intercept data from a NodeMCU device. Following this, we implemented ECC to secure data transmission, showcasing its viability as a lightweight yet robust security solution. Our research not only highlights the pressing need for enhanced security measures in the IoT ecosystem but also provides a practical framework for securing low-cost microcontroller devices against sophisticated cyber threats. Through our findings, we contribute to the development of more secure, trustworthy IoT architectures, ensuring that these devices can continue to safely serve as integral components of our digital lives. Keywords—NodeMCU, ESP8266, Data theft, Elliptic Curve Cryptography, Man-in-the-middle attack

Designs of Energy Harvester for Wireless Sensor Networks

An essential part of the IoT architecture are wireless sensor networks. There has been extensive study done to identify alternative methods of powering wireless sensor networks because the nodes of wireless sensor networks are mostly power constrained. This study mainly introduces three energy collector solutions that can power wireless sensors. They are an electromagnetic vibration energy harvesting system that powers the light source of the fiber Bragg grating sensor, a solar energy collection system that uses ambient photovoltaic energy, and a wind energy harvester that uses gas elastic vibration to convert wind energy into electrical energy. The solar energy collection system is based on maximum power point tracking, in which the solar panels can provide 3W of power with a high efficiency of up to 96%; the wind energy harvester is composed of a wind generator and a power management unit, capable of operating at wind speeds of 2-9m/s, providing 70mW of power. Besides, the electromagnetic vibration energy harvesting system provides an average power output of approximately 40mW when subjected to acceleration levels greater than 0.05g. These results can serve as solutions for sustainable power output.

Enhancing Security in Internet of Things (IoT) Architecture through Defense-in-Depth Mechanism: A Comprehensive Study

The Internet of Things (IoT) has revolutionized various domains, offering connectivity and data sharing among diverse devices and services. However, this interconnectedness poses significant security challenges, primarily centered around confidentiality, integrity, and availability. This paper investigates the security issues embedded in the layers of IoT architecture, ranging from the perception layer to the application layer. By leveraging the Defense-in-Depth security mechanism, we propose a comprehensive approach to fortify IoT systems against cyber threats. The Defense-in-Depth strategy is illustrated through a layered model, addressing security concerns at the perimeter, host, OS/application, and data layers. The study explores various security measures applicable to each layer, emphasizing the need for a multi-faceted approach to ensure the robustness of IoT security. Keywords: Internet of Things (IoT), Security Issues, Defense-in-Depth Mechanism, Perception Layer, Network Layer, Data Processing Layer, Application Layer, Confidentiality, Integrity, Availability

A Comprehensive Survey on Applications, Challenges, Threats and Solutions in IoT Environment and Architecture

A Comprehensive Survey on Applications, Challenges, Threats and Solutions in IoT Environment and Architecture

IoT system development for heart rhythm monitoring and cardiovascular risk estimation

The research focuses on addressing the global issue of cardiovascular diseases. The key variable under consideration for predicting cardiovascular diseases is heart rate variability (HRV). Leveraging the widespread adoption of IoT in various applications, particularly in the health sector, the study proposes the design and implementation of an IoT system for HRV monitoring. The research unfolded in four methodological phases: exploration and selection of technologies, definition of the IoT architecture, development of the prototype, and verification of its functionality. The implemented IoT system adheres to the conventional 4-layer IoT architecture: capture, storage, analysis, and visualization. Heart rate data is periodically acquired using a heart rate sensor and an Arduino-compatible board. The storage layer employs a non-relational database to store the captured data. The analysis layer extracts metrics related to HRV (High: RR <750 ms, Moderate: RR 750–900 ms, Low: RR >900 ms) by applying and delivering quantitative results from clustering algorithms such as machine learning models to evaluate data distribution. Risk levels indicate specific patient metrics. Thus, a 75-year-old patient exhibits an average HR of 75.56, Avg. RR of 795.42, falling into Cluster 1 with a risk value of 1.0. Similar detailed metrics and risk stratifications are presented for patients aged 68, 46, 37, and 18, demonstrating the system's robustness and efficacy in assessing cardiovascular risk. The visualization layer enables real-time observation of physiological variables, risk metrics, and results from data analytics models. The distinctive features of the results lie in the portability advantages of the IoT system, utilizing free hardware and software tools. This facilitates easy replication and utilization of the proposed system in medical campaigns, specifically for the early detection of cardiac conditions. The portable IoT system, leveraging free tools, enhances predictive capabilities for early cardiovascular risk detection globally

Real-Time Monitoring of Photovoltaic Systems and Control of Electricity Supply for Smart Micro Grid-PV using IoT

This paper aims to develop a photovoltaic (PV) performance monitoring system applied on a micro scale using the Internet of Things (IoT). Previous monitoring systems had limitations in platform flexibility, low-cost devices, hardware complexity, and stability of the data transfer process. For this reason, this research proposes an IoT architecture that uses Arduino devices, mini WIFI and an open-source platform, so that it can be easily developed further. This research also develops innovations in controlling the use of electrical energy sources from PV and utility networks. This monitoring system is applied to PV installations with a capacity of 1KW which is capable of monitoring electrical data in the form of current, voltage, power, energy and frequency obtained from PV panels, batteries, loads and electrical utilities. Monitoring data is displayed in a visual form that can be accessed via web-based and mobile applications. This research has developed an innovation in controlling the use of electrical energy sources from PV and Utility networks by using several sensors as parameters to determine the right time and condition in controlling the use of electrical resources.

An Overview of Iot Architecture Security Issues and Countermeasures

The Internet of Things (IoT) has revolutionized the way we interact with our surroundings. As the number of IoT devices continues to increase, along with limited resources and diverse technologies, the risk of security attacks increases. Therefore, it is important to integrate security measures throughout the development process and system architecture. However, it is crucial to continually assess and update security measures to avoid emerging threats and ensure the confidentiality, integrity, and availability of IoT systems. This study aims to explore security issues in the IoT, highlighting the associated challenges. It examines various threats, attacks, and vulnerabilities that arise within a three-layer architecture and discusses potential solutions to enhance security in each layer. By addressing these concerns, it is possible to establish a secure and reliable foundation for expanding IoT systems.

Software defined networking for internet of things: review, techniques, challenges, and future directions

Security networks as one of the biggest issue for network managers with the exponential growth of devices connected to the internet. Keeping a big and diverse network running smoothly and securely is no easy feat. With this in mind, emerging technologies like software defined networking (SDN) and internet of things (IoT) hold considerable promise for information service innovation in the cloud and big data era. Therefore, this paper describes the model of SDN and the architecture of IoT. Then this review does not only review the research studies in SDN-IoT but also provides an explanation of the SDN-IoT solution in terms of architecture, main consideration, model, and the implementation of SDN controllers for IoT. Finally, this review discusses the challenges and future directions. This paper can be used as a starting point for thinking about how to improve SDN-IoT security and privacy.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

TEDA: a trusted execution environment-and-blockchain-based data protection architecture for Internet of Things

TEDA: a trusted execution environment-and-blockchain-based data protection architecture for Internet of Things

IoT-Based Framework for Digital Twins in the Industry 5.0 Era.

Digital twins are considered the next step in IoT-based cyber-physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.