Internet Of Things Architecture Research Articles

Retracted: Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Retracted: Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform.

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution's potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.

INCEPTION TO SENSOR AND IoT TECHNOLOGY

The Internet of Things (IoT) is the recent research topic. The term “Internet of Things" (IoT) is a term that was conceived in 1999 by Kevin Ashton. The Internet of Things (IoT) is a network of physical objects or "things" that are embedded with devices, software, sensors, and networking in order to provide more value. By sharing data with the manufacturer, and by providing service operator or other devices that are linked, every single element is different. It is distinguishable due to its embedded computing system, but able to work together in the current internet. The IoT is being implemented in every hook and corner of industry and personal life of people. Governments are taking note and examining IoT from different perspectives. One aspect is how IoT system can help with performance, analytics and other tasks. Intelligence and decision making are the most important aspects. It combines various sensors and objects to interact directly with one another without human interference. This research paper focuses on the state of the art of IoT architecture. It has four significant layers, the Perception Layer, Network Layer, Middle Layer and Application Layer. The sensors can provide information on emerging health-care issues. Sensors have recently been considered as one of the most rapidly developing fields in physics, electronics, and biotechnology, and it is the one that has benefited the most from advances in individual microelectronics, optical, and computer sciences technologies.

A Conventional IoT Architecture: Precision Agriculture as Domain of Application

A Conventional IoT Architecture: Precision Agriculture as Domain of Application

Security in the Internet of Things (Iot): A Review, Block Chain Applications

The rise of smart technologies in various domains such as homes, cities, and other interconnected systems has propelled the Internet of Things (IoT) into a realm of immense influence, potential, and expansion. Cisco Inc. has forecasted a staggering 50 billion connected devices by 2020, underscoring the rapid proliferation of IoT. Despite this growth, a significant concern looms over the security of these IoT devices, as many are susceptible to hacking and compromise. Their limited computational, storage, and networking capabilities render them more vulnerable compared to traditional endpoint devices like smartphones, tablets, and computers.This paper examines and categorizes the major security challenges facing IoT devices. It reviews prevalent security issues within the layered architecture of IoT, encompassing networking, communication, and management protocols. The paper also delineates the security prerequisites for IoT and surveys current attacks, threats, and state-of-the-art solutions. Additionally, it presents a comparative analysis of IoT security challenges and existing solutions found in the literature.Furthermore, the paper delves into the potential of blockchain, the foundational technology behind bitcoin, as a pivotal tool for addressing numerous security issues in IoT. It discusses how blockchain can enhance security in IoT ecosystems. The paper concludes by outlining open research problems and ongoing challenges in IoT security, underscoring the need for continued innovation in this domain.

A Systematic Review of IoT Security: Research Potential, Challenges, and Future Directions

The Internet of Things (IoT) encompasses a network of physical objects embedded with sensors, software, and data processing technologies that can establish connections and exchange data with other devices and systems via the Internet. IoT devices are incorporated into various products, ranging from ordinary household items to complex industrial appliances. Despite the increasing demand for IoT, security concerns have impeded its development. This article systematically reviews IoT security research, focusing on vulnerabilities, challenges, technologies, and future directions. It surveys 171 recent publications in the field, providing a comprehensive discussion on the development status, challenges, and solutions in IoT. The article outlines IoT architecture patterns and typical features, evaluates existing limitations, and explores strategies for enhancing IoT security. Additionally, the article delves into known IoT attacks and discusses the security countermeasures and mechanisms to address these challenges. It explores the functional requirements of IoT security and explores related technologies and standards. Finally, the article discusses potential future research directions in IoT security.

Security and Privacy of Internet of Things: A Review of Challenges and Solutions

The Internet of Things (IoT) is a global and comprehensive network that monitors and controls the physical world by collecting, processing, and analysing data derived from IoT devices. Globally, the IoT is emerging as a trend bringing intelligence and automation to a variety of application domains, presenting both opportunities and security challenges. It is used in various fields, including medical care, smart grids, home automation, smart cities, etc. As the number of devices connected to the IoT increases, there is an increased risk of cyber-attacks, data breaches, and other malicious activities. This means that it is essential to have secure protocols and encryption methods in place to protect the data being exchanged. This paper identifies and synthesizes security issues related to the IoT. Security threats across different layers of the IoT architecture are discussed first, followed by identifying prevalent security and privacy issues. A discussion is also provided on security countermeasures.

Design cognitive IoT architecture framework for immersive visual technologies of air quality monitoring systems

Design cognitive IoT architecture framework for immersive visual technologies of air quality monitoring systems

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building’s energy efficiency.

An Automated Electronic System in a Motorized Wheelchair for Telemonitoring: Mixed Methods Study Based on Internet of Things.

Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user. This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring. We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts. We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users. The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.

MSLShard: An efficient sharding-based trust management framework for blockchain-empowered IoT access control

The Internet of Things (IoT) is widely used in modern smart areas such as smart cities, but data security remains a significant challenge. Blockchain-based IoT access control addresses data security issues by preventing unauthorized devices from accessing limited IoT resources. However, most existing blockchain-based access control schemes still have scalability and security issues. A lightweight and secure framework, MSLShard, is proposed for the blockchain-based access control via sharding, which consists of two parts. First, an adaptive network sharding scheme based on network distance, node credibility, and access frequency is designed in the cloud-edge-end collaboration-based IoT architecture to reduce nodes' storage pressure and improve the scheme's scalability. Second, the trust management based on multidimensional subjective logic explicitly portrays the heterogeneity among edge nodes to motivate cooperation and further improve the security of the blockchain. Experimental results show that MSLShard can achieve high scalability and security in large-scale IoT environments compared to existing methods.

A Systematic Roadmap on Privacy, Security, Trust, Identity Management, and Resilience: Wireless Sensor Networks and Internet of Things Architectures

As everything around us will be linked to the net in many ways via the Internet of Things (IoTs) and compared to the standard Internet, new forms of problems and complications can arise. Huge IoTs experiments are currently under way, most of which concern its sight, supporting technology, software, or facilities. Recently, a limited studies have comprehensively defined the security requirements such as privacy concerns, security, and trusting in the IoTs that are deemed special to the future net, these terms need to be discussed and addressed via numerous scholars and research groups as well. This article surveyed through 102 references from popular literature databases to explore the features\ properties that define the distinctive IoT relating to forthcoming privacy, security and trust issues. Then created security requirements that were triggered by the mentioned properties. This article examined the privacy, security, trust and resilience components of the three most popular IoT architectures in consideration of the requirements as well. Also, this survey contributed to the state-of-the-art security issues for embedded devices in Internet of Things world including provide a comparative table of well-known secure routing protocols and their countermeasures to well-known attacks on Open Systems Interconnection (OSI) structure of Wireless Sensor Networks (WSNs) within Internet of Things world. Finally, this survey identifies a number of study gaps that will serve as the foundation for future research.

Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN

Abstract Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate.

Deep Reinforcement Learning for Resource Demand Prediction and Virtual Function Network Migration in Digital Twin Network

Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where Network Function Virtualization (NFV) can dynamically provide Virtualized Network Functions (VNFs) for IoT devices. In NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs). However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of IoT network. In this paper, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm Bidirectional Gated Recurrent Unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the digital twin IoT network.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\\%), decreased node shutdown time (45 %), and less infrastructure cost (48\\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

More Accurate Cost Estimation for Internet of Things Projects by Adaptation of Use Case Points Methodology

This paper adapts the Use Case Points method to estimate the size and development effort required for the Internet of Things systems. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems. It was validated using a case study of three IoT systems, demonstrating its applicability and effectiveness in estimating the development effort required for IoT projects. However, the results also highlight the need for further improvements, particularly given the absence of historical data sets for IoT projects. Our future work will focus on gathering such datasets and further refining the proposed model.

Potentializing Green-IoT, AI and 6G Technologies in Computational Thinking

Computational thinking is a problem-solving approach that involves breaking down complex problems into smaller, more manageable parts and identifying patterns and relationships between different components whereas Green-IoT and Artificial Intelligence (AI) in 6G environment are important approaches for potentializing an effective Computational Thinking solution. AI approaches involve developing algorithms that can learn from data, make predictions and decisions, and perform complex tasks without explicit programming. When it comes to Green-IoT, both computational thinking and Artificial Intelligence can be used to analyze and interpret the massive amounts of data generated by connected devices using 6G communication networks. This can lead to more intelligent and efficient use of resources, as well as improved automation of various processes. The combination of Green-IoT, AI and 6G Technologies in computational thinking has the potential to transform various industries including the educational one and improve the quality of life for individuals is expected to lead to new applications and services that are more intelligent, efficient, and personalized. This research presents a review on computational thinking, particularly, we first give an overview of essential parameters through Green-IoT enabled sensor technologies under 5G, 6G environment and Artificial Intelligence. We then present an overview of requirements of basic architecture for IoT and AI based computational thinking system considering key attributes to potentialize social applications and discussing their strengths and weaknesses in the context of framework for community services. Finally, we present various security threats to an AI-based architecture to design robust and resilient computational thinking services system needed in the context of social networking.

Potentializing Green-IoT, AI and 6G Technologies in Computational Thinking

Computational thinking is a problem-solving approach that involves breaking down complex problems into smaller, more manageable parts and identifying patterns and relationships between different components whereas Green-IoT and Artificial Intelligence (AI) in 6G environment are important approaches for potentializing an effective Computational Thinking solution. AI approaches involve developing algorithms that can learn from data, make predictions and decisions, and perform complex tasks without explicit programming. When it comes to Green-IoT, both computational thinking and Artificial Intelligence can be used to analyze and interpret the massive amounts of data generated by connected devices using 6G communication networks. This can lead to more intelligent and efficient use of resources, as well as improved automation of various processes. The combination of Green-IoT, AI and 6G Technologies in computational thinking has the potential to transform various industries and improve the quality of life for individuals is expected to lead to new applications and services that are more intelligent, efficient, and personalized. However, it is important to consider the ethical implications of using these technologies and to ensure that they are used in a responsible and transparent manner. This research presents a review on computational thinking, particularly, we first give an overview of essential parameters through Green-IoT enabled sensor technologies under 5G, 6G environment and Artificial Intelligence. We then present a basic architecture for IoT and AI based computational thinking system considering key requirements to potentialize social applications discussing their strengths and weaknesses in the context of framework for community services. Finally, we explored various security threats for Artificial Intelligence based architecture and their solutions with a comprehensive methodology to design robust and resilient computational thinking services system needed in the context of social networking and related issues.

Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques

In the era of Industry 4.0, the digital capture of products has become a critical aspect, which prompts the need for reliable inspection methods. In the current technological landscape, the Internet of Things (IoT) holds significant value, especially for industrial devices that require seamless communication with local and cloud computing servers. This research focuses on the advancements made in roundness measurement techniques for industrial automation by leveraging an IoT architecture, computer vision, and image processing. The interconnectedness enables the efficient collection of feedback information, meeting the demands of closed-loop manufacturing. The accuracy and performance of assemblies heavily rely on the roundness of specific workpiece components. In order to address this problem, automated inspection methods are needed. A new method of computer vision for measuring and inspecting roundness is proposed in this paper. This method uses a non-contact method that takes into account all points on the contours of measured objects, making it more accurate and practical than conventional methods. The system developed by AMMC Laboratory captures Delrin work images and analyzes them using a specially designed 3SMVI system based on Open CV with Python script language. The system can measure and inspect several rounded components in the same part, including external frames and internal holes. It is calibrated to accommodate various units of measurement and has been tested using sample holes within the surface feature of the workpiece. According to the results of both techniques, there is a noticeable difference ranging from 2.9 µm to 11.6 µm. However, the accuracy of the measurements can be enhanced by utilizing a high-resolution camera with proper lighting. The results were compared to those obtained using a computer measurement machine (CMM), with a maximum difference of 8.7%.

Privacy-Preserving Federal Learning Chain for Internet of Things

The expansion of Internet of Things (IoT) spawns large on-device machine learning demands, while the machine learning can be a hard task for resource constrained IoT terminals with fragmented dataset. Federal learning (FL), which aims to build a joint model across multiple devices, IoT-FL has now become a promising path for learning on terminals. In broad FL fields, current server-client pattern cannot jump out of the third party self-trustless problem, and recent researches suggest that even sharing training results may also reveal the raw data sets. Homomorphic encryption (HE) is a powerful method in privacy preserving, while so far it is hard to applied HE into multi-party computing (MPC) scenarios including FL. Combining with the existing IoT architecture, in this paper, we customize a scheme (FL chain) dedicated for the privacy and trustiness issues in IoT-FL scenarios, which integrates blockchain smart contract and HE. Differ from traditional schemes, our FL chain is highly adaptive with current IoT architecture and it is the first scheme that applied HE into IoT-FL privacy preserving. Theoretical analysis and experimental results prove the feasibility of FL chain.