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1
Han, Jungsu, Sun Park, and JongWon Kim. "Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds." Electronics 9, no. 6 (2020): 969. http://dx.doi.org/10.3390/electronics9060969.
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With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.
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Zhang, Yongqiang, Hongchang Yu, Wanzhen Zhou, and Menghua Man. "Application and Research of IoT Architecture for End-Net-Cloud Edge Computing." Electronics 12, no. 1 (2022): 1. http://dx.doi.org/10.3390/electronics12010001.
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At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (IoT) application scenarios. Based on the IoT architecture, an IoT edge computing (EC-IoT) reference architecture is proposed, which contained three layers: The end edge, the network edge and the cloud edge. Furthermore, the key technologies of the application of artificial intelligence (AI) technology in the EC-IoT reference architecture is analyzed. Platforms for different EC-IoT reference architecture edge locations are classified by comparing IoT edge computing platforms. On the basis of EC-IoT reference architecture, an industrial Internet of Things (IIoT) edge computing solution, an Internet of Vehicles (IoV) edge computing architecture and a reference architecture of the IoT edge gateway-based smart home are proposed. Finally, the trends and challenges of EC-IoT are examined, and the EC-IoT architecture will have very promising applications.
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Kalyashina, Anna, Yuri Smirnov, Valeriy Esov, Maxim Kuznetsov, and Oksana Dmitrieva. "Enhancing IoT systems through Cloud-Fog-Edge architectures challenges and opportunities." E3S Web of Conferences 583 (2024): 06012. http://dx.doi.org/10.1051/e3sconf/202458306012.
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This article examines the deployment and implications of Cloud- Fog-Edge architectures in Internet of Things (IoT) systems, highlighting their significance in enhancing data management and system security across diverse sectors. As IoT ecosystems expand, the necessity for architectures that efficiently handle large volumes of data and ensure real-time processing capabilities becomes paramount. The Cloud-Fog-Edge architecture addresses these needs by distributing computing resources across three layers—cloud, fog, and edge—each optimized for specific tasks within the IoT workflow. We discuss the challenges and solutions associated with interoperability in such multi-layered systems, emphasizing the need for standardized communication protocols and data formats to facilitate seamless interactions between heterogeneous devices and platforms. Furthermore, the article delves into the critical aspects of security within these architectures, outlining strategies for robust data encryption, access management, regular security updates, and comprehensive network activity monitoring to safeguard against unauthorized access and cyber threats. The integration of Cloud-Fog-Edge architectures not only promises enhanced operational efficiency and scalability but also significantly boosts the adaptability of IoT systems to meet evolving technological and operational demands. By providing a detailed analysis of the functionalities, integration challenges, and security practices associated with each architectural layer, this article contributes to a deeper understanding of how Cloud-Fog-Edge frameworks can be optimized to bolster the reliability, efficiency, and security of modern IoT environments.
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Fereira, Rolden, Chathurika Ranaweera, Kevin Lee, and Jean-Guy Schneider. "Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture." Sensors 23, no. 13 (2023): 6039. http://dx.doi.org/10.3390/s23136039.
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Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures.
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Aleisa, Mohammed A., Abdullah Abuhussein, Faisal S. Alsubaei, and Frederick T. Sheldon. "Novel Security Models for IoT–Fog–Cloud Architectures in a Real-World Environment." Applied Sciences 12, no. 10 (2022): 4837. http://dx.doi.org/10.3390/app12104837.
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With the rise of the Internet of Things (IoT), there is a demand for computation at network edges because of the limited processing capacity of IoT devices. Fog computing is a middle layer that has appeared to address the latency issues between the Internet of things (IoT) and the cloud. Fog computing is becoming more important as companies face increasing challenges in collecting and sending data from IoT devices to the cloud. However, this has led to new security and privacy issues as a result of the large number of sensors in IoT environments as well as the massive amount of data that must be analyzed in real time. To overcome the security challenges between the IoT layer and fog layer and, thus, meet the security requirements, this paper proposes a fine-grained data access control model based on the attribute-based encryption of the IoT–Fog–Cloud architecture to limit the access to sensor data and meet the authorization requirements. In addition, this paper proposes a blockchain-based certificate model for the IoT–Fog–Cloud architecture to authenticate IoT devices to fog devices and meet the authentication requirements. We evaluated the performance of the two proposed security models to determine their efficiency in real-life experiments of the IoT–Fog–Cloud architecture. The results demonstrate that the performance of the IoT–Fog–Cloud architecture with and without the blockchain-based certificate model was the same when using one, two, or three IoT devices. However, the performance of the IoT–Fog–Cloud architecture without the access control model was slightly better than that of the architecture with the model when using one, two, or three IoT devices.
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Belem Pacheco, Luis, Eduardo Pelinson Alchieri, and Priscila Mendez Barreto. "Device-Based Security to Improve User Privacy in the Internet of Things †." Sensors 18, no. 8 (2018): 2664. http://dx.doi.org/10.3390/s18082664.
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The use of Internet of Things (IoT) is rapidly growing and a huge amount of data is being generated by IoT devices. Cloud computing is a natural candidate to handle this data since it has enough power and capacity to process, store and control data access. Moreover, this approach brings several benefits to the IoT, such as the aggregation of all IoT data in a common place and the use of cloud services to consume this data and provide useful applications. However, enforcing user privacy when sending sensitive information to the cloud is a challenge. This work presents and evaluates an architecture to provide privacy in the integration of IoT and cloud computing. The proposed architecture, called PROTeCt—Privacy aRquitecture for integratiOn of internet of Things and Cloud computing, improves user privacy by implementing privacy enforcement at the IoT devices instead of at the gateway, as is usually done. Consequently, the proposed approach improves both system security and fault tolerance, since it removes the single point of failure (gateway). The proposed architecture is evaluated through an analytical analysis and simulations with severely constrained devices, where delay and energy consumption are evaluated and compared to other architectures. The obtained results show the practical feasibility of the proposed solutions and demonstrate that the overheads introduced in the IoT devices are worthwhile considering the increased level of privacy and security.
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Tanweer, Alam. "Internet of things: review, architecture and applications." Computer Science and Information Technologies 3, no. 1 (2022): 31–38. https://doi.org/10.11591/csit.v3i1.pp31-38.
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Devices linked to the internet of things (IoT) may communicate with one another in several settings. Furthermore, rather of relying on an existing centralized system, users may develop their own network by using wireless capabilities. This kind of network is known as a wireless mobile ad hoc network. The mobile ad-hoc network (MANET) enables IoT devices to connect with one another in an unstructured networked environment. IoT devices may connect, establish linkages, and share data on a continuous basis. In this system, the cloud's purpose is to store and analyze data acquired from IoT devices. One of the most significant challenges in cloud computing has been identified as information security, and its resolution will result in an even bigger increase in cloud computing usage and popularity in the future. Finally, the goal of this project is to create a framework for facilitating communication between IoT devices in a Cloud and MANET context. Our major contribution is a ground-breaking research initiative that combines cloud computing with the MANET and connects the internet of things. This research might be used to the IoT in the future.
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Zhurylo, Oleh, and Oleksii Liashenko. "Architecture and iot security systems based on fog computing." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 1 (27) (July 2, 2024): 54–66. http://dx.doi.org/10.30837/itssi.2024.27.054.
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The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.
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Researcher. "ENHANCING IOT SYSTEMS WITH SCALABLE CLOUD ARCHITECTURES FOR REAL-TIME DATA PROCESSING." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 774–86. https://doi.org/10.5281/zenodo.14229605.
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The development and application of Internet of Things (IoT) systems coupled with scalable cloud architectures for real-time data processing are examined in detail in this extensive essay. From data ingestion to security concerns, the paper explores the basic difficulties enterprises face while overseeing extensive IoT deployments. It explores the elements of cloud-based architecture, highlighting the crucial roles played by processing frameworks, storage options, and data intake levels. The essay discusses edge analytics integration and emphasizes how it can improve privacy, optimize bandwidth, and reduce latency. Important scalability issues in both horizontal and vertical dimensions are also covered in the article, along with implementation best practices, including microservices architecture and thorough monitoring techniques. Additionally, it looks at the security environment of IoT cloud infrastructures, including compliance frameworks, data protection strategies, and device security.
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Alam, Tanweer. "Internet of things: review, architecture and applications." Computer Science and Information Technologies 3, no. 1 (2022): 31–38. http://dx.doi.org/10.11591/csit.v3i1.p31-38.
Full textAbstract:
Devices linked to the internet of things (IoT) may communicate with one another in several settings. Furthermore, rather of relying on an existing centralized system, users may develop their own network by using wireless capabilities. This kind of network is known as a wireless mobile ad hoc network. The mobile ad-hoc network (MANET) enables IoT devices to connect with one another in an unstructured networked environment. IoT devices may connect, establish linkages, and share data on a continuous basis. In this system, the cloud's purpose is to store and analyze data acquired from IoT devices. One of the most significant challenges in cloud computing has been identified as information security, and its resolution will result in an even bigger increase in cloud computing usage and popularity in the future. Finally, the goal of this project is to create a framework for facilitating communication between IoT devices in a Cloud and MANET context. Our major contribution is a ground-breaking research initiative that combines cloud computing with the MANET and connects the internet of things. This research might be used to the IoT in the future.
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Tanweer Alam. "Internet of things: review, architecture and applications." Computer Science and Information Technologies 3, no. 1 (2022): 31–38. http://dx.doi.org/10.11591/csit.v3i1.pp31-38.
Full textAbstract:
Devices linked to the internet of things (IoT) may communicate with one another in several settings. Furthermore, rather of relying on an existing centralized system, users may develop their own network by using wireless capabilities. This kind of network is known as a wireless mobile ad hoc network. The mobile ad-hoc network (MANET) enables IoT devices to connect with one another in an unstructured networked environment. IoT devices may connect, establish linkages, and share data on a continuous basis. In this system, the cloud's purpose is to store and analyze data acquired from IoT devices. One of the most significant challenges in cloud computing has been identified as information security, and its resolution will result in an even bigger increase in cloud computing usage and popularity in the future. Finally, the goal of this project is to create a framework for facilitating communication between IoT devices in a Cloud and MANET context. Our major contribution is a ground-breaking research initiative that combines cloud computing with the MANET and connects the internet of things. This research might be used to the IoT in the future.
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Jang, Chi Young, Dal Hwan Yoon, Myung Kee Jang, et al. "Implementing an Edge-IoT System for Real-Time Information Gathering of RTOs : Implementing Architecture for IoT Data Collection." Forum of Public Safety and Culture 34 (September 30, 2024): 33–50. http://dx.doi.org/10.52902/kjsc.2024.34.33.
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In this study, an IoT architecture is implemented to collect and determine real-time status information for remote maintenance of heat storage thermal oxidizer. IoT architecture systems interface with PLC (Programmable Logic Control), which controls RTO operation monitoring, and transmit real-time data to remote servers, and channel data received through IoT is analyzed to build a database and visualize the characteristics of RTO devices. IoT architecture implementation for real-time monitoring, failure determination, and maintenance of heat storage combustion oxidation facilities interface IoT circuitry to the PLC control panel that controls monitoring. Compatibility and information security are considered to facilitate maintenance at a distance. The received data is segmented for each channel and analyzed with a visualization algorithm to detect abnormality in segmentation data. At this time, the visualized data are judged as normal, insufficient, or warning according to the threshold value. The common software stack used in existing IoT systems is necessary for computing, portability, and ease of management that allows data processing to be moved between the edge and the cloud. When an edge node interacts with a specific cloud backend in the monitoring PLC of multiple RTO devices, the network bandwidth between the edge and the cloud presents a bottleneck of large-scale data transfer. In addition, in a clustered system of many nodes where data is transferred between the edge and the cloud, data can be lost forever due to a malfunction of a single edge node. Especially in the case of node failure or intermittent long-distance network connection problems, it is necessary to implement local fault tolerance to preserve system state locally at the edge. To address this problem, this paper proposes CEFIoT, a new fault-tolerant architecture for IoT applications by adopting state-of-the-art cloud technology and also deploying it to edge computing. The CEFIoT architecture consists of three layers: (i) Application Isolation, (ii) Data Transport, and (iii) Multi-Cluster Management layer. Based on this tiered design, the architecture allows computing deployments on edges or clouds without source code modifications. In addition to real-time status data for each RTO facility, information management on the histories of individual devices and parts is required, and facility data according to abnormal situations are essential for preservation through time series data learning. However, since it can be difficult to reproduce each abnormal situation, it is necessary to virtually reproduce the abnormal situation through facility modeling through normal state data.
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Hamdan, Salam, Moussa Ayyash, and Sufyan Almajali. "Edge-Computing Architectures for Internet of Things Applications: A Survey." Sensors 20, no. 22 (2020): 6441. http://dx.doi.org/10.3390/s20226441.
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The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.
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R.Deepika, .Kalpana R.A, Sree R.Sharmikha, and S.Meera. "Fog Assisted Computing Architecture for Healthcare IOT." Recent Trends in Cloud Computing and Web Engineering 5, no. 3 (2023): 11–16. https://doi.org/10.5281/zenodo.8207698.
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<em>IOT generates associate new knowledge that may be processed exploitation cloud computing. except for time period remote health watching applications, the delay caused by transferring information to the cloud and back to the applying is unacceptable .Due to continuous watching of Iot devices Cloud storage got accumulated with bulk of junk files. process the information in cloud takes terribly while that makes slow alerts. To reduce the delay, the patient’s real-time data is processed by fog layer using fog node by event based triggering. In Fog Layer edge level computing is performed based on the threshold values the health status of respective patient is analyzed . If the status is getting abnormal then the Fog layer sends the health data to the Cloud. Then the emergency message will be sent to Hospital Ambulance , Doctor and relatives based on the type of event triggered.</em>
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Xiang, Feng, and Ye Fa Hu. "Cloud Manufacturing Resource Access System Based on Internet of Things." Applied Mechanics and Materials 121-126 (October 2011): 2421–25. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2421.
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The cloud manufacturing (CloudM) resource access solution of Internet of Things (IOT) application was discussed. According to resource access requirements, a kind of CloudM resource access architecture based on IOT was described, as well as several key technologies in the architecture. Several key issues such as classification of resource information accesses, access processes were studied. Related explanations about QoS information expanding, manufacturing capacity information constructing, EPC information collection were illustrated. With magnetic bearing as an example, its conceptual information model and QoS expanded information described by PML were given.
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Ms., Pragati Patil, and Anil Pimpalapure Dr. "Decentralized Security Architecture Based on Software Defined Networking (SDN) in Blockchain for IOT Network." International Journal of Engineering Research and Science 10, no. 6 (2024): 23–31. https://doi.org/10.5281/zenodo.15203773.
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<strong>Abstract</strong><strong>—</strong> Cloud computing has emerged as a major technology for delivering infrastructure and data service needs at cheap cost, with minimum effort and great scalability, and has therefore been widely adopted in the IT sector. Although there has been a tremendous increase in Cloud Computing use, information security issues have yet to be entirely addressed. To address the current challenges, this paper proposes a decentralised security architecture for the IoT network in the smart city based on Software Defined Networking (SDN) combined with blockchain technology that relies on the three core technologies of SDN, Blockchain, and Fog as well as mobile edge computing to detect attacks in the IoT network more effectively. Our findings show that the suggested decentralised security architecture outperforms centralised and distributed security architectures in the IoT ecosystem and takes less time to prevent threats. Our results also show that the architecture might be used with the IoT ecosystem as a security detection component that monitors and analyses the whole IoT ecosystem's traffic data to identify and prevent possible threats.
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Al-Joboury, Istabraq M., and Emad H. Hemiary. "Internet of Things Architecture Based Cloud for Healthcare." Iraqi Journal of Information & Communications Technology 1, no. 1 (2018): 18–26. http://dx.doi.org/10.31987/ijict.1.1.7.
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The Internet of Things (IoT) contains smart devices placed in different environments, connected with each other across networks and Internet. The integration between Things and Cloud Computing (CC) for monitoring and permanent storage is required for future IoT applications. Therefore, this paper proposes IoT architecture based Cloud for healthcare network when patients are remotely monitored by their family and physicians. This proposed architecture is different from the traditional IoT architecture that consists of Things, getaways, middleware, and application layers which in turn need connectivity insurance between them. The proposed architecture is designed and configured using Cisco Packet Tracer version 7.0 over two sites: Site 'A' located at smart home and site 'B' located at the smart hospital. The results show that the IoT based Cloud enhances the patient life style by using smart sensors and mobile application, as well as the physicians can remotely monitor the data in real time.
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Abbas, Qaisar, and Abdullah Alsheddy. "Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis." Sensors 21, no. 1 (2020): 56. http://dx.doi.org/10.3390/s21010056.
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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.
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Sarabia-Jácome, David, Sergio Giménez-Antón, Athanasios Liatifis, Eduard Grasa, Marisa Catalán, and Dimitrios Pliatsios. "Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration." Applied Sciences 14, no. 6 (2024): 2300. http://dx.doi.org/10.3390/app14062300.
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Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In view of this, IoT networks require innovative network architectures that offer scalability, security, and adaptability. The Recursive InterNetwork Architecture (RINA) is a clean slate network architecture that provides a scalable, secure, and flexible framework for interconnecting computers. SDN technology is becoming a de facto solution to overcome network requirements, making RINA adoption difficult. This paper presents an architecture for integrating RINA with SDN technologies to lower the barriers of adopting RINA in IoT environments. The architecture relies on a RINA-based distributed application facility (DAF), a RINA southbound driver (SBI), and the RINA L2VPN. The RINA-based DAF manages RINA nodes along the edge–fog–cloud continuum. The SBI driver SDN enables the hybrid centralized management of SDN switches and RINA nodes. Meanwhile, the RINA L2VPN allows seamless communication between edge nodes and the cloud to facilitate the data exchange between network functions (NFs). Such integration has enabled a progressive deployment of RINA in current IoT networks without affecting their operations and performance.
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Hashembeigi, Fatemeh, Fatemeh Moshiri, and Abbas Asosheh. "Introducing and Evaluating an Architectural Model for Smart Tourism Health Insurance based on IoT and Cloud." Journal of Health and Biomedical Informatics 10, no. 1 (2023): 28–40. http://dx.doi.org/10.34172/jhbmi.2023.12.
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Introduction: Tourism as one of the programs to earn money leads to economic growth; however, due to the non -providing of medical and health services by insurance organizations, the Iranian tourism industry has faced a recession. Therefore, this study aimed to provide a smart tourism health insurance architecture in the context of the Internet of Things (IoT) and cloud to serve tourists. Method : This practical study was conducted to achieve a smart tourism health insurance architecture. First, its requirements were determined by reviewing the related literature and open interview with four experts from Iran Insurance Corporation. Then, by examining different types of tourism health insurance architectural models, a proposed service -oriented layered architecture was presented on the platform of IoT and cloud. Finally, the proposed architecture was evaluated by the ATAM method . Results: The proposed architecture has two components (i.e., the requester and the service provider) with user, network, cloud, and database layers that are connected in a service - oriented manner. Also, the evaluation of the proposed architecture by the ATAM method showed that it can respond to the health needs of tourists. Conclusion: This architecture is presented on the platform of IoT and cloud that provides an intelligent background, and by recording people's data in personal risk profiles, it facilitates providing special tariffs and reducing tourism costs. Therefore, tourists will be able to travel with more confidence in controlling their health status.
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Tole, Sutikno, and Thalmann Daniel. "Insights on the internet of things: past, present, and future directions." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 6 (2022): 1399–420. https://doi.org/10.12928/telkomnika.v20i6.22028.
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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process’s ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.
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Fernández-Cerero, Damián, Jorge Yago Fernández-Rodríguez, Juan A. Álvarez-García, Luis M. Soria-Morillo, and Alejandro Fernández-Montes. "Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things." Sensors 19, no. 13 (2019): 3026. http://dx.doi.org/10.3390/s19133026.
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The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.
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Yang, Hyunsik, and Younghan Kim. "Design and Implementation of Fast Fault Detection in Cloud Infrastructure for Containerized IoT Services." Sensors 20, no. 16 (2020): 4592. http://dx.doi.org/10.3390/s20164592.
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The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method.
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Xu, Rongxu, Lei Hang, Wenquan Jin, and Dohyeun Kim. "Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments." Actuators 10, no. 8 (2021): 197. http://dx.doi.org/10.3390/act10080197.
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The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment.
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Loseto, Giuseppe, Floriano Scioscia, Michele Ruta, et al. "Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems." Sensors 22, no. 6 (2022): 2166. http://dx.doi.org/10.3390/s22062166.
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Artificial Intelligence (AI) in Cyber-Physical Systems allows machine learning inference on acquired data with ever greater accuracy, thanks to models trained with massive amounts of information generated by Internet of Things devices. Edge Intelligence is increasingly adopted to execute inference on data at the border of local networks, exploiting models trained in the Cloud. However, the training tasks on Edge nodes are not supported yet with flexible dynamic migration between Edge and Cloud. This paper proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. Notable features include: (i) containerized architecture enabling training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy; and (ii) microservice encapsulation of each architectural module, allowing a direct mapping with Commercial-Off-The-Shelf (COTS) components. Grounding on the proposed architecture: (i) a prototype has been realized with commodity hardware leveraging open-source software technologies; and (ii) it has been then used in a small-scale intelligent manufacturing case study, carrying out experiments. The obtained results validate the feasibility and key benefits of the approach.
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Farooq, Omar, and Parminder Singh. "A Secure Architecture For Resource Constrained Iot Environment." Journal of University of Shanghai for Science and Technology 23, no. 09 (2021): 248–64. http://dx.doi.org/10.51201/jusst/21/09535.
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One of the most exciting emerging concepts nowadays is the Internet of Things. However, digital currency has run into issues with how quickly it has been adopted. The number of IoT devices is increasing exponentially, and presently we have more than 20000 million objects connected to the network. The amount of data and complexity circulating across networks is also growing exponentially. IoT plays a measure role in this growth rate of IoT data traffic, resulting in a significant rise in data traffic reaching the cloud or data centers. The response time of IoT systems is affected by the growth of data traffic as this may not be appropriate for sensitive environments. This paper presents a framework and a machine learning approach for the data management of IoT edge-cloud environments with resource-constrained IoT applications. In this paper, the security aspect has also been discussed for the resource-constrained IoT framework.
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Detti, Andrea, Hidenori Nakazato, Juan Antonio Martínez Navarro, et al. "VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service." Sensors 21, no. 19 (2021): 6546. http://dx.doi.org/10.3390/s21196546.
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Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.
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Subramanian, Sunthar. "IoT and Edge Computing for Smart Manufacturing: Architecture and Future Trends." International Journal of Engineering and Computer Science 13, no. 10 (2024): 26504–22. http://dx.doi.org/10.18535/ijecs/v13i10.4922.
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The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies. Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.
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Tanweer, Alam, and Benaida Mohamed. "Blockchain, Fog and IoT Integrated Framework: Review, Architecture and Evaluation." Technology Reports of Kansai University 62, no. 2 (2020): 81–92. https://doi.org/10.5281/zenodo.3871191.
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In the next-generation computing, the role of cloud, internet, and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store and process big data in the cloud. The fogging acts as an interface between cloud and IoT. The IoT nodes are also known as fog nodes, these nodes are able to access anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing new blockchains based integrated framework in the architecture of the IoT is one of the emerging approaches to solving the issue of communication security among the IoT public nodes. This research explores a novel approach to integrate blockchain technology with the fog and IoT networks and provides communication security to the internet of smart devices. The framework is tested and implemented in the IoT network. The results are found positive.
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Ansari, Shahista, and Abhijit N. Banubakode. "Things to Cloud : IOT." Journal of IoT Security and Smart Technologies 1, no. 3 (2022): 1–12. http://dx.doi.org/10.46610/jisst.2022.v01i03.001.
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The Internet of Things is causing a big revolution, which is a new reality that has replaced traditional life with modernized life. Smart objects have made our lives easy. The IoT is virtually present in every aspect of life. Much research in IoT made this possible, along with technology and electronic devices such as chips, sensors, etc. which have greatly contributed to this new revolution and helped to recognize the full potential of IoT. This paper refers to the various domains in IoT and some of their examples, as well as the detailed architecture of IoT with examples. IoT generates huge amounts of data. Hence, the role of big data and cloud computing as tools in solving IoT problems and efficient working. Some issues and challenges related to the IoT have also been discussed, such as security and privacy.
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Bartsch, Witali, and Michael Hübner. "Universelle Referenzarchitektur für eine sichere cloudbasierte Automation." atp magazin 61, no. 9 (2019): 72–82. http://dx.doi.org/10.17560/atp.v61i9.2428.
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In the first of a series of articles, we introduce the term “Symbiotic Security” to denote an ideal architecture where all essential components (e.g. hardware, software or networks) contribute to raising the architectural security bar. The growing importance of cloud computing for secure and resilient automation and its intended independence from hardware to accommodate all platforms have led us to observe a disconnect between well-known cloud service providers and manufacturers of embedded devices or IoT: the unsolved problem of initial enrolment. After elaborating on the root cause of this gulf we present a non-invasiveextension and implementation of a cloud IoT reference architecture for an automated, mutually authenticated and encrypted roll-out of IoT nodes. To also enable automated key management without human intervention, the system refrains from using any static secrets usually employed by the hardware vendors – a longstanding point of criticism. Despite our practical choice of a target platform, the idea itself is uniform across such environments given their inherent similarities.
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Yang, Hyunsik, and Younghan Kim. "Design and Implementation of High-Availability Architecture for IoT-Cloud Services." Sensors 19, no. 15 (2019): 3276. http://dx.doi.org/10.3390/s19153276.
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For many vertical Internet of Things (IoT) applications, the high availability is very important. In traditional cloud systems, services are usually implemented with the same level of availability in which the fault detection and fault recovery mechanisms are not aware of service characteristics. In IoT-cloud, various services are provided with different service characteristics and availability requirements. Therefore, the existing cloud system is inefficient to optimize the availability method and resources to meet service requirements. To address this issue, this paper proposes a high availability architecture that is capable of dynamically optimizing the availability method based on service characteristics. The proposed architecture was verified through an implementation system based on OpenStack, and it was demonstrated that the system was able to achieve the target availability while optimizing resources, in contrast with existing architectures that use predefined availability methods.
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Ahuja, Sanjay P., and Nathan Wheeler. "Architecture of Fog-Enabled and Cloud-Enhanced Internet of Things Applications." International Journal of Cloud Applications and Computing 10, no. 1 (2020): 1–10. http://dx.doi.org/10.4018/ijcac.2020010101.
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Fog computing has been rising in popularity over the last few years due in part to the many benefits that Fog confers upon Internet of Things (IoT) applications. Fog Computing extends the Cloud to the IoT devices. In this paper, the author explore IoT, Fog, and Cloud, as well as the benefits that are possible and have been realized by utilizing the 3 technologies in a 3-tier architecture. A reference architecture is provided, applications of the 3-tier architecture from the literature are discussed, and recommendations are made for future work.
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Dineva, Kristina, and Tatiana Atanasova. "Design of Scalable IoT Architecture Based on AWS for Smart Livestock." Animals 11, no. 9 (2021): 2697. http://dx.doi.org/10.3390/ani11092697.
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In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behaviour, reproduction, emotional state, and stress levels of animals. The AWS services used, and their specific tasks related to the proposed architecture are explained in detail. A stress test was performed to prove the data ingesting and processing capability of the proposed architecture. Experimental results proved that the proposed architecture using AWS automated scaling mechanisms and IoT devices are fully capable of processing the growing amount of data, which in turn allow for meeting the required needs of the constantly expanding number of CPS systems.
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Marah, Bockarie Daniel, Zilong Jing, Tinghuai Ma, et al. "Smartphone Architecture for Edge-Centric IoT Analytics." Sensors 20, no. 3 (2020): 892. http://dx.doi.org/10.3390/s20030892.
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The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.
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Ghani, Naveed, Aznida Abu Bakar Sajak, Rehan Qureshi, Megat Farez Azril Zuhairi, and Zaid Mujaiyid Putra Ahmad Baidowi. "A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 564. http://dx.doi.org/10.62527/joiv.8.2.2187.
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The Internet of Things (IoT) has become an integral part of our daily lives, growing exponentially from a facility to a necessity. IoT has been utilized extensively through cloud computing and has proven an excellent technology for deploying in various fields. The data generated by the IoT devices gets transmitted to the cloud for processing and storage. However, with this approach, there are specific issues like latency, energy, computation resources availability, bandwidth, heterogeneity, storage, and network failure. To overcome these obstacles, fog computing is utilized as a middle tier. Fog computing gathers and processes the generated data closer to the user end before transmitting it to the cloud. This paper aims to conduct a structured review of the current state of fog computing and its architectures deployed across multiple industries. This paper also focuses on the implementation and critical parameters for introducing fog computing in IoT-cloud architecture. A detailed comparative analysis has been carried out for 5 different architectures considering various crucial parameters to identify how the quality of service and quality of experience for end users can be optimized. Finally, this paper looks at the multiple challenges that fog computing faces in a structured six-level approach. These challenges will also lead the way for future research in resource management, green computing, and security.
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Alonso, Ricardo S., Inés Sittón-Candanedo, Roberto Casado-Vara, Javier Prieto, and Juan M. Corchado. "Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture." Sustainability 12, no. 14 (2020): 5706. http://dx.doi.org/10.3390/su12145706.
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The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller.
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Khanna, Abhirup. "An architectural design for cloud of things." Facta universitatis - series: Electronics and Energetics 29, no. 3 (2016): 357–65. http://dx.doi.org/10.2298/fuee1603357k.
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In recent times the world has seen an exponential rise in the number of devices connected to the internet. This widespread expansion of the internet and growth in the number of interconnected devices has lead to the rise of many new age technologies. Internet of Things (IoT) being one of them allows devices to communicate with one another that are connected through the internet. It provides a new way of looking towards pervasive computing wherein "things" be it sensors, embedded devices, actuators or humans interact with one another. But currently IoT is facing a number of challenges related to scalability, interoperability, storage capacity, processing power and security which all act as a deterrent for its practical implementation. Cloud computing, the buzzword of the IT industry, suits best to handle all these challenges, thus leading towards the integration of cloud and IoT. In this paper, we present a layered architecture for Cloud of Things, i.e. the amalgamation of cloud computing and internet of things. The architecture provides a scalable approach for IoT as it allows dynamic addition of n-number of "things". Moreover, the architecture allows the end users to host their applications onto the cloud and access IoT systems remotely. Towards the end, the paper discusses a use case that proves the correctness of the proposed architecture.
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Zhu, Wenlong, Changli Zhou, and Linmei Jiang. "A Trusted Internet of Things Access Scheme for Cloud Edge Collaboration." Electronics 13, no. 6 (2024): 1026. http://dx.doi.org/10.3390/electronics13061026.
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With the rapid popularization of current Internet of Things (IoT) technology and 5G networks, as well as the continuous updating of new service lifestyles and businesses, the era of big data processing for the IoT has arrived. However, centralizing all data for processing in the cloud can lead to issues such as communication latency and privacy breaches. To solve these problems, edge computing, as a new network architecture close to terminal data sources and supporting low latency services, has gradually emerged. In this context, cloud edge collaborative computing has become an important network architecture. With the changing security requirements and communication methods of cloud edge collaborative network architecture, traditional authentication key agreement protocols are no longer applicable. Therefore, a new IoT authentication and key agreement protocol needs to be designed to solve this problem. This study proposes an IoT accessible solution for cloud edge collaboration. This scheme adopts a chaotic mapping algorithm to achieve efficient authentication. It ensures the anonymity and untraceability of users. Following this, we conducted strict security verification using BAN logic and Scyther tools. Through experimental comparative analysis, the research results show that the protocol performs better than other schemes while ensuring security. This indicates that the protocol can achieve efficient authentication and key negotiation in cloud edge collaborative network architecture, providing a secure and reliable solution for the accessibility of the IoT.
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صالح حسين العوامي and موسى فرج النجار. "A Proposed Unified Standard IoT-Based Architecture Based on 7-Layers Framework (Case Applied: Car Accident System)." Journal of Pure & Applied Sciences 21, no. 4 (2022): 75–80. http://dx.doi.org/10.51984/jopas.v21i4.2129.
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Internet of Things (IoT) is one of the famous applications of internet commuting and of cloud computing, and it is a widely used applications by organizations or by individuals, i.e., smart city, smart home, etc. There are many different IoT platforms that have been developed and used by IoT cloud providers to provide services to their subscribers. These different platforms lead to difficulty understand of each platform by developers. So, the need of IoT standard system is highly required to mitigate the diversity of building IoT-based system using different software layers. In this paper, a unified IoT-based architecture has been proposed based on 7-layers framework to be as a de facto standard to the most of IoT-based applications. Moreover, the proposed architecture has been applied using the car accident system which is a known application of IoT based application using CubCarbon Wireless Senser Networks (WSN) simulator. The proposed architecture shows the consistency between its 7-layers and promising to be as a considered unified IoT architecture.
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Dorave, Jayantkumar, and Ritesh Sadiwala. "Uplink and Downlink Variation in Drone Technology for Cloud, Edge, Fog and Smart Dust Integrated IoT Architecture: Demonstrated Over WSNs." Journal of Physics: Conference Series 2089, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/2089/1/012023.
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Abstract IoT concepts are heavily applicable in drone communication integrated with different network architecture for optimization. Distributing the burden allows more IoT devices to execute calculations, rather than everything being done on the cloud. There are numerous IoT designs that have emerged as a result of this. By relocating calculations away from the cloud, these designs make use of the enhanced processing capacity of the devices. Based on our needs, we’ve limited it down to four architectures, each of which we have discussed for optimized flow useful in drone technology. We have also applied the one live dataset for the test drone using raspberry pi processor system powered with for end-to-end drone communication establishment. The analysis of downlink and uplink were studied for time analysis for IoT architecture using drone cell characteristics. New technology makes it possible to implement drone cell (DC) connectivity, which is highly flexible and cost-effective for the gathering of Internet-of-things (IoT) data when terrestrial networks are not yet accessible. DC’s flight path has a substantial impact on data collecting systems.
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Chandra Sekhara Reddy Adapa. "Cloud-based Master Data Management: Transforming Enterprise Data Strategy." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1057–65. https://doi.org/10.32628/cseit25112436.
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Cloud-based Master Data Management (MDM) solutions are revolutionizing how organizations handle critical data assets in today's rapidly evolving digital landscape. These solutions offer enhanced scalability, flexibility, and real-time access capabilities that traditional on-premises systems struggle to match. By leveraging advanced technologies such as artificial intelligence, machine learning, and microservices architecture, cloud-based MDM enables organizations to achieve superior data quality, improved operational efficiency, and better business outcomes. The integration of blockchain technology and IoT capabilities is further transforming how enterprises maintain data integrity and manage connected device information. Modern MDM platforms incorporate comprehensive security frameworks and intelligent data management features, allowing organizations to maintain robust governance while adapting to changing business requirements. Through API-led connectivity and microservices architecture, these solutions provide enhanced integration capabilities and architectural flexibility. The advancement of cloud-native architectures, combined with AI-driven automation and analytics, positions cloud-based MDM as a crucial enabler of digital transformation and business success.
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Belli, Laura, Simone Cirani, Luca Davoli, Gianluigi Ferrari, Lorenzo Melegari, and Marco Picone. "Applying Security to a Big Stream Cloud Architecture for the Internet of Things." International Journal of Distributed Systems and Technologies 7, no. 1 (2016): 37–58. http://dx.doi.org/10.4018/ijdst.2016010103.
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The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.
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Falah, Muhammad Fajrul, Yohanes Yohanie Fridelin Panduman, Sritrusta Sukaridhoto, et al. "Comparison of cloud computing providers for development of big data and internet of things application." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1723–30. https://doi.org/10.11591/ijeecs.v22.i3.pp1723-1730.
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The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.
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Belli, Laura, Simone Cirani, Luca Davoli, et al. "A Scalable Big Stream Cloud Architecture for the Internet of Things." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (2015): 26–53. http://dx.doi.org/10.4018/ijssoe.2015100102.
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The Internet of Things (IoT) will consist of billions (50 billions by 2020) of interconnected heterogeneous devices denoted as “Smart Objects:” tiny, constrained devices which are going to be pervasively deployed in several contexts. To meet low-latency requirements, IoT applications must rely on specific architectures designed to handle the gigantic stream of data coming from Smart Objects. This paper propose a novel Cloud architecture for Big Stream applications that can efficiently handle data coming from Smart Objects through a Graph-based processing platform and deliver processed data to consumer applications with low latency. The authors reverse the traditional “Big Data” paradigm, where real-time constraints are not considered, and introduce the new “Big Stream” paradigm, which better fits IoT scenarios. The paper provides a performance evaluation of a practical open-source implementation of the proposed architecture. Other practical aspects, such as security considerations, and possible business oriented exploitation plans are presented.
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Kakade, Manoj Subhash, Anupama Karuppiah, Mayank Mathur, et al. "Multitask Scheduling on Distributed Cloudlet System Built Using SoCs." Journal of Systemics, Cybernetics and Informatics 21, no. 1 (2023): 61–72. http://dx.doi.org/10.54808/jsci.21.01.61.
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With the emergence of IoT, new computing paradigms have also emerged. Initial IoT systems had all the computing happening on the cloud. With the emergence of Industry 4.0 and IoT being the major building block, clouds are not the only solution for data storage and analytics. Cloudlet, Fog Computing, Edge Computing, and Dew Computing models are now available, providing similar capabilities as the cloud. The term cloudlet was introduced first in 2011, but research in this area has picked up only over the past five years. Unlike clouds, which are built with powerful server-class machines and GPUs, cloudlets are usually made using simpler devices such as SoCs. In this paper, we propose a complete novel distributed architecture for cloudlets, and we are also proposing algorithms for data storage and task allocation across various nodes in the cloudlet. This cloudlet system was built using Qualcomm Snapdragon 410c. We have analyzed the architecture and the algorithm for varying workloads, bandwidth and data storage. The primary aim of the algorithm and the architecture is to ensure uniform processing and data loads across the nodes of the system.
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Jalowiczor, Jakub, Jan Rozhon, and Miroslav Voznak. "Study of the Efficiency of Fog Computing in an Optimized LoRaWAN Cloud Architecture." Sensors 21, no. 9 (2021): 3159. http://dx.doi.org/10.3390/s21093159.
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The technologies of the Internet of Things (IoT) have an increasing influence on our daily lives. The expansion of the IoT is associated with the growing number of IoT devices that are connected to the Internet. As the number of connected devices grows, the demand for speed and data volume is also greater. While most IoT network technologies use cloud computing, this solution becomes inefficient for some use-cases. For example, suppose that a company that uses an IoT network with several sensors to collect data within a production hall. The company may require sharing only selected data to the public cloud and responding faster to specific events. In the case of a large amount of data, the off-loading techniques can be utilized to reach higher efficiency. Meeting these requirements is difficult or impossible for solutions adopting cloud computing. The fog computing paradigm addresses these cases by providing data processing closer to end devices. This paper proposes three possible network architectures that adopt fog computing for LoRaWAN because LoRaWAN is already deployed in many locations and offers long-distance communication with low-power consumption. The architecture proposals are further compared in simulations to select the optimal form in terms of total service time. The resulting optimal communication architecture could be deployed to the existing LoRaWAN with minimal cost and effort of the network operator.
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Javed, Asad, Jérémy Robert, Keijo Heljanko, and Kary Främling. "IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications." Journal of Grid Computing 18, no. 1 (2020): 57–80. http://dx.doi.org/10.1007/s10723-019-09498-8.
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AbstractThe evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the problem of separate software stacks between the edge and the cloud with no unified fault-tolerant management, hindering dynamic relocation of data processing. In such systems, the data must also be preserved from being corrupted or duplicated in the case of intermittent long-distance network connectivity issues, malicious harming of edge devices, or other hostile environments. Within this context, the contributions of this paper are threefold: (i) to propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications by adapting our earlier Cloud and Edge Fault-Tolerant IoT (CEFIoT) layered design. We address the fault tolerance issue by employing the Apache Kafka publish/subscribe platform as the unified data replication solution. We also deploy Kubernetes for fault-tolerant management, combined with the federated scheme, offering a single management interface and allowing automatic reconfiguration of the data processing pipeline, (ii) to formulate functional and non-functional requirements of our proposed solution by comparing several IoT architectures, and (iii) to implement a smart buildings use case of the ongoing Otaniemi3D project as proof-of-concept for assessing IoTEF capabilities. The experimental results conclude that the architecture minimizes latency, saves network bandwidth, and handles both hardware and network connectivity based failures.
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Verma, Jyotsna. "Enabling Internet of Things through Sensor Cloud: A Review." Scalable Computing: Practice and Experience 22, no. 4 (2021): 445–62. http://dx.doi.org/10.12694/scpe.v22i4.1878.
Full textAbstract:
With the inception of the Internet of Things (IoT), wireless technology found a new outlook where the physical objects can interact with each other and can sense the environment. The IoT has found its way in the real world and has connected billions of devices throughout the world. However, its limitations, such as limited processing capability, storage capability, security and privacy issues, and energy constraints prevent the IoT system to be properly utilized by the real-world applications. Hence, the integration of IoT with various emerging technologies like big data, software defined networks, machine learning, fog computing, sensor cloud, etc., will make the IoT system a more powerful technology. The sensor cloud provides an open, secure, flexible, large storage and a computational capable infrastructure which makes the ensemble architecture of IoT and sensor cloud more efficient. An extensive review of the IoT system enabled sensor cloud is presented in the paper, and with this context, the paper attempts to summarize the sensor cloud infrastructure along with its challenges. In addition, the paper presents the possible integrated architecture of the IoT and the sensor cloud which enables the network to be properly utilized. Further, the importance of integrating these two promising technologies and research challenges associated with it is also identified. Finally, the paper analyses and discusses the motivation behind the ensemble system along with future research direction.
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Khandelwal, Deversh, and Manjot Kaur Bhatia. "IOT Architecture, Challenges and Opportunities." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1137–40. http://dx.doi.org/10.22214/ijraset.2022.47047.
Full textAbstract:
Abstract: The Internet of Things, as defined by the Internet itself, is the connection of embedded computing devices embedded differently within the existing infrastructure. According to IoT, soon our planet will contain the largest number of devices connected to the Internet. This paper is a discussion of the trending term 'Internet of Things' challenges and opportunities, as well as the formation of IoT, which describes widely used technologies such as M2M communications, cloud computing, IPv6, and RFID technologies, thus describing the use of IoT different fields. Finally, there is a brief review of all possible IoT applications