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1
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 (December 20, 2022): 1. http://dx.doi.org/10.3390/electronics12010001.
Повний текст джерелаАнотація:
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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Shafiq, Muhammad, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz, and Adnan Tahir. "SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8864301.
Повний текст джерелаАнотація:
The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed system, three different parameters are selected: IoT Device Security (IDSC), IoT Device Storage (IDST), and IoT Device Communication Speed (IDCS). We also find out the most significant parameters from the given set of parameters. It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.
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Lee, Dongkyu, Hyeongyun Moon, Sejong Oh, and Daejin Park. "mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications." Sensors 20, no. 12 (June 12, 2020): 3337. http://dx.doi.org/10.3390/s20123337.
Повний текст джерелаАнотація:
As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.
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Moon, Hyeongyun, and Daejin Park. "An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications." Electronics 10, no. 17 (August 28, 2021): 2088. http://dx.doi.org/10.3390/electronics10172088.
Повний текст джерелаАнотація:
The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.
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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 (August 13, 2021): 197. http://dx.doi.org/10.3390/act10080197.
Повний текст джерелаАнотація:
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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Bansal, Malti, and Harshit. "IoT based Edge Computing." December 2020 2, no. 4 (January 5, 2021): 204–10. http://dx.doi.org/10.36548/jtcsst.2020.4.005.
Повний текст джерелаАнотація:
Edge computing is a new way of calculating where most computer and storage devices are located on the internet, near mobile devices, sensors, end users, and internet of things devices. This physical approach improves delays, bandwidth, trust and survival.
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Firouzi, Ramin, Rahim Rahmani, and Theo Kanter. "Context-based Reasoning through Fuzzy Logic for Edge Intelligence." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 17–25. http://dx.doi.org/10.5383/juspn.15.01.003.
Повний текст джерелаАнотація:
With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.
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Xu, Rongxu, Wenquan Jin, Yonggeun Hong, and Do-Hyeun Kim. "Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform." Electronics 10, no. 12 (June 18, 2021): 1460. http://dx.doi.org/10.3390/electronics10121460.
Повний текст джерелаАнотація:
In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.
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Borra, Praveen, Mahidhar Mullapudi, Harshavardhan Nerella, and Lalith Kumar Prakashchand. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology 13, no. 6 (August 30, 2024): 8–12. http://dx.doi.org/10.35940/ijeat.f4519.13060824.
Повний текст джерелаАнотація:
This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.
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Anarbayevich, Abdurakhmanov Ravshan. "HARNESSING EDGE COMPUTING FOR ENHANCED SECURITY AND EFFICIENCY IN IOT NETWORKS." American Journal of Applied Science and Technology 4, no. 3 (March 1, 2024): 18–23. http://dx.doi.org/10.37547/ajast/volume04issue03-04.
Повний текст джерелаАнотація:
The Internet of Things (IoT) has revolutionized numerous sectors by enabling seamless connectivity and data exchange among devices. However, with the proliferation of IoT devices, concerns regarding security vulnerabilities and network efficiency have escalated. This article explores the integration of edge computing within IoT networks as a solution to address these challenges. Edge computing, by bringing computation closer to the data source, offers enhanced security measures and alleviates bandwidth constraints, thereby optimizing network performance. Through a comprehensive review of existing literature and case studies, this article examines the implementation of edge computing techniques such as data filtering, encryption, and decentralized processing to fortify IoT systems against cyber threats. Furthermore, it delves into the benefits of edge computing in improving real-time data analytics, reducing latency, and facilitating autonomous decision-makingin IoT environments. By elucidating the synergistic relationship between IoT and edge computing technologies, this article aims to provide valuable insights for researchers, practitioners, and policymakers in advancing the security and efficiency of IoT deployments.
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Zhai, Zhongyi, Ke Xiang, Lingzhong Zhao, Bo Cheng, Junyan Qian, and Jinsong Wu. "IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment." Sensors 20, no. 8 (April 17, 2020): 2294. http://dx.doi.org/10.3390/s20082294.
Повний текст джерелаАнотація:
The edge-based computing paradigm (ECP) becomes one of the most innovative modes of processing distributed Interneit of Things (IoT) sensor data. However, the edge nodes in ECP are usually resource-constrained. When more services are executed on an edge node, the resources required by these services may exceed the edge node’s, so as to fail to maintain the normal running of the edge node. In order to solve this problem, this paper proposes a resource-constrained smart service migration framework for edge computing environment in IoT (IoT-RECSM) and a dynamic edge service migration algorithm. Based on this algorithm, the framework can dynamically migrate services of resource-critical edge nodes to resource-rich nodes. In the framework, four abstract models are presented to quantificationally evaluate the resource usage of edge nodes and the resource consumption of edge service in real-time. Finally, an edge smart services migration prototype system is implemented to simulate the edge service migration in IoT environment. Based on the system, an IoT case including 10 edge nodes is simulated to evaluate the proposed approach. According to the experiment results, service migration among edge nodes not only maintains the stability of service execution on edge nodes, but also reduces the sensor data traffic between edge nodes and cloud center.
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Li, Xiaoshan, and Mingming Chen. "RT-Cabi: an Internet of Things based framework for anomaly behavior detection with data correction through edge collaboration and dynamic feature fusion." PeerJ Computer Science 10 (October 21, 2024): e2306. http://dx.doi.org/10.7717/peerj-cs.2306.
Повний текст джерелаАнотація:
The rapid advancement of Internet of Things (IoT) technologies brings forth new security challenges, particularly in anomaly behavior detection in traffic flow. To address these challenges, this study introduces RT-Cabi (Real-Time Cyber-Intelligence Behavioral Anomaly Identifier), an innovative framework for IoT traffic anomaly detection that leverages edge computing to enhance the data processing and analysis capabilities, thereby improving the accuracy and efficiency of anomaly detection. RT-Cabi incorporates an adaptive edge collaboration mechanism, dynamic feature fusion and selection techniques, and optimized lightweight convolutional neural network (CNN) frameworks to address the limitations of traditional models in resource-constrained edge devices. Experiments conducted on two public datasets, Edge-IIoT and UNSW_NB15, demonstrate that RT-Cabi achieves a detection accuracy of 98.45% and 90.94%, respectively, significantly outperforming existing methods. These contributions not only validate the effectiveness of the RT-Cabi model in identifying anomalous behaviors in IoT traffic but also offer new perspectives and technological pathways for future research in IoT security.
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Mahalingam, Anandaraj, Ganeshkumar Perumal, Gopalakrishnan Subburayalu, Mubarak Albathan, Abdullah Altameem, Riyad Saleh Almakki, Ayyaz Hussain, and Qaisar Abbas. "ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks." Sensors 23, no. 19 (September 23, 2023): 8044. http://dx.doi.org/10.3390/s23198044.
Повний текст джерелаАнотація:
The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.
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Gudnavar, Anand, and Keerti Naregal. "Edge Computing in Internet of Things (IoT): Enhancing IoT Ecosystems through Distributed Intelligence." Advancement of IoT in Blockchain Technology and its Applications 2, no. 3 (August 7, 2023): 1–7. http://dx.doi.org/10.46610/aibtia.2023.v02i03.001.
Повний текст джерелаАнотація:
This paper explores Edge Computing in the Internet of Things (IoT) and its pivotal role in enhancing IoTecosystems through distributed intelligence. We present the architecture of edge computing systems for IoT, emphasizing edge devices, edge servers, and seamless cloud integration. Investigating edge analytics and data processing, we showcase real-time analysis at the edge, reducing reliance on distant cloud resources and enhancing responsiveness. Resource management strategies, including task offloading and load balancing, optimize system performance. Addressing security concerns, we propose solutions for edge device security, data privacy, and trust management. Case studies demonstrate successful edge computing applications in diverse IoT scenarios, validating their transformative impact. Nonetheless, challenges remain, requiring further research. In conclusion, "Edge Computing in the Internet of Things (IoT)" empowers IoT ecosystems with distributed intelligence, revolutionizing IoT-driven applications across domains
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Lee, Seunghwan, Linh-An Phan, Dae-Heon Park, Sehan Kim, and Taehong Kim. "EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX." Applied Sciences 12, no. 1 (December 23, 2021): 140. http://dx.doi.org/10.3390/app12010140.
Повний текст джерелаАнотація:
With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.
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Ciuffoletti, Augusto. "Stateless IoT." Information 11, no. 2 (February 4, 2020): 85. http://dx.doi.org/10.3390/info11020085.
Повний текст джерелаАнотація:
Energy consumption is a relevant matter in the design of IoT applications. Edge units—sensors and actuators—save energy by operating intermittently. When idle, they suspend their operation, losing the content of the onboard memory. Their internal state, needed to resume their work, is recorded on external storage: in the end, their internal operation is stateless. The backend infrastructure does not follow the same design principle: concentrators, routers, and servers are always-on devices that frustrate the energy-saving operation of edge devices. In this paper, we show how serverless functions, asynchronously invoked by the stateless edge devices, are an energy-saving option. We introduce a basic model for system operation and energy footprint evaluation. To demonstrate its soundness, we study a simple use case, from the design to a prototype.
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Jin, Wenquan, Yong-Geun Hong, Jaeseung Song, Jaeho Kim, and Dohyeun Kim. "Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks." Sensors 23, no. 19 (October 6, 2023): 8282. http://dx.doi.org/10.3390/s23198282.
Повний текст джерелаАнотація:
The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.
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Jang, Chi Young, Dal Hwan Yoon, Myung Kee Jang, Jae Wan Jeong, Ji Heon Kim, Min Woo Kim, Hoon Min Park, and Tae Young Lim. "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.
Повний текст джерелаАнотація:
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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Rahman, Mohammad Saidur, Ibrahim Khalil, Xun Yi, Mohammed Atiquzzaman, and Elisa Bertino. "A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living." ACM Transactions on Internet Technology 22, no. 3 (August 31, 2022): 1–25. http://dx.doi.org/10.1145/3453171.
Повний текст джерелаАнотація:
Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.
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C.P, Vandana, and Dr Ajeet A. Chikkamannur. "IOT future in Edge Computing." International Journal of Advanced Engineering Research and Science 3, no. 12 (2016): 148–54. http://dx.doi.org/10.22161/ijaers/3.12.29.
Повний текст джерела
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Zhang, Jinnan, Changqi Lu, Gang Cheng, Teng Guo, Jian Kang, Xia Zhang, Xueguang Yuan, and Xin Yan. "A Blockchain-Based Trusted Edge Platform in Edge Computing Environment." Sensors 21, no. 6 (March 18, 2021): 2126. http://dx.doi.org/10.3390/s21062126.
Повний текст джерелаАнотація:
Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios.
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Li, Borui, Wei Dong, Gaoyang Guan, Jiadong Zhang, Tao Gu, Jiajun Bu, and Yi Gao. "Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads." ACM Transactions on Sensor Networks 17, no. 3 (June 21, 2021): 1–23. http://dx.doi.org/10.1145/3442363.
Повний текст джерелаАнотація:
Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.
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Petri, Ioan, Omer Rana, Yacine Rezgui, and Fodil Fadli. "Edge HVAC Analytics." Energies 14, no. 17 (September 2, 2021): 5464. http://dx.doi.org/10.3390/en14175464.
Повний текст джерелаАнотація:
Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.
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Sahu, Ranu, Shivam Tiwari, Paras Soni, and Vandana Jaiswal. "IoT Techniques: Exploring Edge Computing Challenges and Ethical Implications in Interconnected Device Systems." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 06 (November 25, 2023): 9033–42. http://dx.doi.org/10.15680/ijirset.2023.1206160.
Повний текст джерелаАнотація:
The Internet of Things (IoT) denotes a system where various devices and items are interconnected, gathering and sharing data through built-in sensors and communication systems. Edge Computing: The emergence of edge computing within IoT calls for immediate data processing at its origin, introducing complexities in effective distributed computing and consistent data management. Ethical Concerns: With the expanding use of IoT across various industries, navigating ethical issues related to data privacy, user consent, and appropriate utilization becomes more intricate and essential. The proposed system integrates advanced IoT techniques with optimized edge computing strategies to address immediate data processing challenges and implement robust ethical safeguards, ensuring efficient distributed computing and responsible data management across interconnected devices. Two Advantages of purposed system are IoT Benefits: The Internet of Things (IoT) fosters smooth connections between devices, boosting efficiency in operations and facilitating instant monitoring and decision-making. Utilizing IoT functionalities allows enterprises to extract valuable insights from data analytics, resulting in enhanced product innovation and enriched customer interactions. Edge Computing Benefits: Edge computing diminishes delays by handling data near its origin, improving response speeds and enabling swift decisions without over-reliance on centralized infrastructures. Integrating edge computing into IoT setups reduces data traffic, resulting in financial savings and streamlined network performance. Advantages of Ethical Considerations: Tackling ethical issues within IoT builds user confidence, encouraging wider acceptance of interconnected systems and emphasizing conscientious data handling. Giving precedence to ethical aspects can pave the way for consistent regulations and standards, establishing a basis for trustworthy and clear IoT implementations. Benefits of the Proposed System: Merging sophisticated IoT methodologies with edge computing approaches bolsters system dependability, adaptability, and efficiency, catering to dynamic data handling needs adeptly. By embedding strong ethical measures, the envisioned system reassures stakeholders, upholding adherence to standards while championing ethical data practices. The suggested system embodies a holistic strategy that combines cutting-edge IoT and edge computing methods, emphasizing ethical aspects, to tackle pivotal issues related to data handling, network optimization, and ethical data stewardship in interconnected device environments.
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Byun, Siwoo. "Replicated Data Management Using Scaled Segment Chain in Unstable IoT Environments." Webology 19, no. 1 (January 20, 2022): 4286–98. http://dx.doi.org/10.14704/web/v19i1/web19282.
Повний текст джерелаАнотація:
IoT edge gateway reduces cloud computing's overload that redirect sensor data to remote servers. For reliable and efficient IoT gateway, column-based flash memory has become a reasonable storage due to its space efficiency and compression performance. This paper introduces recent IoT network and edge computing technology. It proposes efficient replication management called Context-mapped Segment Submirroring to support stable data services for sensor data in the edge-based IoT environment. Sensor context scaling and chained segment submirroring schemes are presented to improve the reliability and performance using IoT edge gateway. In the chained submirroring scheme, the sensor data are kept in the space-efficient storage of IoT edge. Consequently, sensor data transmission and mirroring storage cost can be minimized. The simulation results show that the proposed scheme outperforms the traditional scheme in respect of operation throughput and its response time.
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Kim, Svetlana, Jieun Kang, and YongIk Yoon. "Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment." Electronics 10, no. 17 (September 3, 2021): 2156. http://dx.doi.org/10.3390/electronics10172156.
Повний текст джерелаАнотація:
With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS) degrade due to restricted resources of edge computing and higher execution latency due to congestion. To handle offloaded tasks in a rapidly changing dynamic environment, finding an optimal target server is still challenging. Therefore, a new cooperative offloading method to control edge computing resources is needed to allocate limited resources between distributed edges efficiently. This paper suggests the LODO (linked-object dynamic offloading) algorithm that provides an ideal balance between edges by considering the ready state or running state. LODO algorithm carries out tasks in the list in the order of high correlation between data and tasks through linked objects. Furthermore, dynamic offloading considers the running status of all cooperative terminals and decides to schedule task distribution. That can decrease the average delayed time and average power consumption of terminals. In addition, the resource shortage problem can settle by reducing task processing using its distributions.
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Judvaitis, Janis, Rihards Balass, and Modris Greitans. "Mobile IoT-Edge-Cloud Continuum Based and DevOps Enabled Software Framework." Journal of Sensor and Actuator Networks 10, no. 4 (October 30, 2021): 62. http://dx.doi.org/10.3390/jsan10040062.
Повний текст джерелаАнотація:
This research aims to provide a high-level software framework for IoT-Edge-Cloud computational continuum-based applications with support for mobile IoT and DevOps integration utilizing the Edge computing paradigms. This is achieved by dividing the system in a modular fashion and providing a loosely coupled service and module descriptions for usage in the respective system layers for flexible and yet trustworthy implementation. The article describes the software architecture for a DevOps-enabled Edge computing solution in the IoT-Edge-Cloud computational continuum with the support for flexible and mobile IoT solutions. The proposed framework is validated on an intelligent transport system use case in the rolling stock domain and showcases the improvements gained by using the proposed IoT-Edge-Cloud continuum framework.
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Babar, Mohammad, and Muhammad Sohail Khan. "ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems." International Journal of Distributed Sensor Networks 17, no. 7 (July 2021): 155014772110353. http://dx.doi.org/10.1177/15501477211035332.
Повний текст джерелаАнотація:
Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.
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Huang, Hongyang, Mohammed Dauwed, Morched Derbali, Imran Khan, Sun Li, Kai Chen, and Sangsoon Lim. "An Optimized Approach for Industrial IoT Based on Edge Computing." Wireless Communications and Mobile Computing 2022 (July 9, 2022): 1–15. http://dx.doi.org/10.1155/2022/3918207.
Повний текст джерелаАнотація:
The Internet of Things (IoT) is an information network that connects gadgets and sensors to allow new autonomous tasks. The Industrial Internet of Things (IIoT) refers to the integration of IoT with industrial applications. Some vital infrastructures, such as water delivery networks, use IIoT. The scattered topology of IIoT and resource limits of edge computing provide new difficulties to traditional data storage, transport, and security protection with the rapid expansion of the IIoT. In this paper, a recovery mechanism to recover the edge network failure is proposed by considering repair cost and computational demands. The NP-hard problem was divided into interdependent major and minor problems that could be solved in polynomial time by using the Benders decomposition technique and cutting plane approximation. To ensure the nonincreasing character of the Benders upper limit, a local branching method was also added to improve the convergence. Simulation results indicated that the proposed method is superior to the existing method and has better overall performance.
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Xu, Zhirong, Ming Cai, Xiaoyan Li, Tianlei Hu, and Qianshu Song. "Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks." Sensors 19, no. 9 (May 5, 2019): 2078. http://dx.doi.org/10.3390/s19092078.
Повний текст джерелаАнотація:
Wireless sensor networks have been attracting research attention for the past decade and will continue to be a hot topic due to the emerging trend of Internet-of-Things (IoT). Edge computing for IoT (Edge-IoT) is a promising framework that can help low-powered sensor networks to conduct complex computational tasks. Different from the existing works that focus on cooperative task execution for edge and sensor networks, in this paper, we investigate the problem of reliable data transmission in edge-aided sensor networks. Firstly, we discuss how edge servers can help to improve the data transmission of sensor networks. Secondly, we propose a forwarding scheme for edge nodes to forward packets according to coverage and corresponding interference. Thirdly, we propose an edge-based error recovery approach. By employing edge servers for data transmission and error recovery, the efficiency and reliability of data transmissions can be largely improved.
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Dauda, Abdulkadir, Olivier Flauzac, and Florent Nolot. "A Survey on IoT Application Architectures." Sensors 24, no. 16 (August 17, 2024): 5320. http://dx.doi.org/10.3390/s24165320.
Повний текст джерелаАнотація:
The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems’ deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management.
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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 (November 11, 2020): 6441. http://dx.doi.org/10.3390/s20226441.
Повний текст джерелаАнотація:
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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Mirani, Akseer Ali, Anshul Awasthi, Niall O’Mahony, and Joseph Walsh. "Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge." IoT 5, no. 4 (September 28, 2024): 608–33. http://dx.doi.org/10.3390/iot5040027.
Повний текст джерелаАнотація:
Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.
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Pham, Van-Nam, VanDung Nguyen, Tri D. T. Nguyen, and Eui-Nam Huh. "Efficient Edge-Cloud Publish/Subscribe Broker Overlay Networks to Support Latency-Sensitive Wide-Scale IoT Applications." Symmetry 12, no. 1 (December 18, 2019): 3. http://dx.doi.org/10.3390/sym12010003.
Повний текст джерелаАнотація:
Computing services for the Internet-of-Things (IoT) play a vital role for widespread IoT deployment. A hierarchy of Edge-Cloud publish/subscribe (pub/sub) broker overlay networks that support latency-sensitive IoT applications in a scalable manner is introduced. In addition, we design algorithms to cluster edge pub/sub brokers based on topic similarities and geolocations to enhance data dissemination among end-to-end IoT devices. The proposed model is designed to provide low delay data dissemination and effectively save network traffic among brokers. In the proposed model, IoT devices running pub/sub client applications periodically send collected data, organized as a hierarchy of topics, to their closest edge pub/sub brokers. Then, the data are processed/analyzed at edge nodes to make controlling decisions promptly replying to the IoT devices and/or aggregated for further delivery to other interested edge brokers or to cloud brokers for long-term processing, analysis, and storage. Extensive simulation results demonstrate that our proposal achieves the best data delivery latency compared to two baseline schemes, a classical Cloud-based pub/sub scheme and an Edge-Cloud pub/sub scheme. Considering the similar Edge-Cloud technique, the proposed scheme outperforms PubSubCoord-alike in terms of relay traffic ratio among brokers. Therefore, our proposal can adapt well to support wide-scale latency-sensitive IoT applications.
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Pal, Souvik, N. Z. Jhanjhi, Azmi Shawkat Abdulbaqi, D. Akila, Abdulaleem Ali Almazroi, and Faisal S. Alsubaei. "A Hybrid Edge-Cloud System for Networking Service Components Optimization Using the Internet of Things." Electronics 12, no. 3 (January 28, 2023): 649. http://dx.doi.org/10.3390/electronics12030649.
Повний текст джерелаАнотація:
The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.
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Alam, Tanweer, Baha Rababah, Arshad Ali, and Shamimul Qamar. "Distributed Intelligence at the Edge on IoT Networks." Annals of Emerging Technologies in Computing 4, no. 5 (December 20, 2020): 1–18. http://dx.doi.org/10.33166/aetic.2020.05.001.
Повний текст джерелаАнотація:
The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.
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Tripathi, Dr Diwakar Ramanuj, Harish Tikam Deshlahare, and Roshan Ramdas Markhande. "Edge-Cloud Continuum: Integrating Edge Computing and Cloud Computing for IOT Applications." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (October 31, 2024): 335–42. http://dx.doi.org/10.22214/ijraset.2024.64517.
Повний текст джерелаАнотація:
Abstract: This research will use a mixed-method approach with qualitative understanding and the quantitative analysis of data to examine how edge and cloud computing blend into the frame of an edge-cloud continuum for IoT applications. It adopts an exploratory and descriptive approach to research in carrying out a holistic assessment of the edge-cloud continuum in efficiency, scalability, and performance. The data required for collection was obtained through simulations using the IoTSim-Osmosis framework and also through the use of other case studies and literature published elsewhere before preparing this document. The data is usual to what has been used in several IoT deployments, including smart cities, the health sector, and industrial automation. Based on those KPIs - latency, 70.83%; bandwidth utilization, 40% reduced; energy consumption, 25% reduced; and task completion rate improved by 18.75% - the edge-cloud continuum significantly outperforms traditional cloud-only systems. Especially a very big reduction in latency is significantly important for real-time applications as it would drive the potential ability to improve responsiveness in those applications, like autonomous cars and smart healthcare. The current study demonstrates that the edge-cloud continuum can efficiently enhance resource allocation and enhance the effectiveness of complex IoT systems. This has wide implications for designing and implementing IoT solutions across industry domains
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Mehmood, M. Yasir, Ammar Oad, Muhammad Abrar, Hafiz Mudassir Munir, Syed Faraz Hasan, H. Abd ul Muqeet, and Noorbakhsh Amiri Golilarz. "Edge Computing for IoT-Enabled Smart Grid." Security and Communication Networks 2021 (July 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/5524025.
Повний текст джерелаАнотація:
Smart grid is a new vision of the conventional power grid to integrate green and renewable technologies. Smart grid (SG) has become a hot research topic with the development of new technologies, such as IoT, edge computing, artificial intelligence, big data, 5G, and so on. The efficiency of SG will be increased by smart embedded devices that have intelligent decision-making ability. Various types of sensors and data sources will collect data of high resolution. One of the vital challenges for IoT is to manage a large amount of data produced by sensors. Sending this massive amount of data directly to the cloud will create problems of latency, security, privacy, and high bandwidth utilization. This issue is addressed by edge computing (EC). In EC, the data are processed at the edge of the network that is near the embedded gadgets. This paper provides a comprehensive review of the smart grid systems, based on IoT and EC. The development in the rising technologies, the framework for EC-IoT-based SG, and requirements to implement the EC-IoT-based SG system are highlighted in the paper. Framework for EC-IoT-based SG is examined, and important requirements to implement the EC-IoT-based SG system are outlined. Finally, some critical issues and challenges faced in the implementation of EC-IoT-based SG systems are identified. Some important open research issues are also identified.
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Ajayi, Oluwashina Joseph, Joseph Rafferty, Jose Santos, Matias Garcia-Constantino, and Zhan Cui. "BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems." IoT 2, no. 4 (October 14, 2021): 610–32. http://dx.doi.org/10.3390/iot2040031.
Повний текст джерелаАнотація:
The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.
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Tawalbeh, Lo’ai, Fadi Muheidat, Mais Tawalbeh, and Muhannad Quwaider. "IoT Privacy and Security: Challenges and Solutions." Applied Sciences 10, no. 12 (June 15, 2020): 4102. http://dx.doi.org/10.3390/app10124102.
Повний текст джерелаАнотація:
Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT.
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Canavese, Daniele, Luca Mannella, Leonardo Regano, and Cataldo Basile. "Security at the Edge for Resource-Limited IoT Devices." Sensors 24, no. 2 (January 17, 2024): 590. http://dx.doi.org/10.3390/s24020590.
Повний текст джерелаАнотація:
The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.
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Kuchuk, Heorhii, and Eduard Malokhvii. "INTEGRATION OF IOT WITH CLOUD, FOG, AND EDGE COMPUTING: A REVIEW." Advanced Information Systems 8, no. 2 (June 4, 2024): 65–78. http://dx.doi.org/10.20998/2522-9052.2024.2.08.
Повний текст джерелаАнотація:
Purpose of review. The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining the transformative impact on computational architectures. Approach to review. Beginning with an overview of IoT's evolution and its surge in global adoption, the paper emphasizes the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting the unique characteristics, advantages, and challenges associated with IoT, cloud computing, edge computing, and fog computing. The discussion delves into the individual strengths and limitations of these technologies, addressing issues such as latency, bandwidth consumption, security, and data privacy. Further, the paper explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. Review results. Challenges related to unreliable data handling and privacy concerns are acknowledged, emphasizing the need for robust security measures and regulatory frameworks. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. Fog computing's intermediary role in enhancing bandwidth, reducing latency, and providing scalability for IoT applications is thoroughly examined. Challenges related to security, authentication, and distributed denial of service in fog computing are acknowledged. The paper also explores innovative algorithms addressing resource management challenges in fog-IoT environments. Conclusions. The survey concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Cybersecurity concerns, energy consumption, and standardization challenges are identified as key areas for future research.
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Shouket, Ahmad Kouchay. "Edge, fog, and cloud computing in IoT-Significance and security concerns." i-manager’s Journal on Cloud Computing 10, no. 1 (2023): 17. http://dx.doi.org/10.26634/jcc.10.1.19210.
Повний текст джерелаАнотація:
Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.
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Xavier, Tiago C. S., Flavia C. Delicato, Paulo F. Pires, Claudio L. Amorim, Wei Li, and Albert Zomaya. "Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation." ACM Transactions on Internet of Things 3, no. 2 (May 31, 2022): 1–28. http://dx.doi.org/10.1145/3488248.
Повний текст джерелаАнотація:
In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.
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BAJRA, Gynejt, Eip RUFATI, Valbon ADEMI, and Vehbi RAMAJ. "EDGE COMPUTING FOR INTERNET OF THINGS: ARCHITECTURES, CHALLENGES AND OPPORTUNITIES." Journal of Natural Sciences and Mathematics of UT-JNSM 9, no. 17-18 (October 10, 2024): 275–83. http://dx.doi.org/10.62792/ut.jnsm.v9.i17-18.p2822.
Повний текст джерелаАнотація:
The Internet of Things (IoT) connects diverse devices to provide digital services globally. Edge computing, a new model, processes data at the network edge for faster responses. This paper discusses IoT architecture, protocols, computing models, and the benefits and challenges they pose. It highlights the need for high-performance IoT applications, especially in critical scenarios, and suggests that Edge computing can enhance efficiency and privacy by processing data where it's produced. Environmental impacts of cloud-based data management and the sustainability of Edge computing are also explored.
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Deng, FangXiao, and A. Gudamu. "Research on Evaluation Method of Sports Events Based on Edge Algorithm." Security and Communication Networks 2021 (August 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/1903699.
Повний текст джерелаАнотація:
In view of the high computational cost and long computational time of IoT edge algorithm in traditional sports event evaluation, this paper optimizes IoT edge algorithm by introducing deep reinforcement learning technology. Set the IoT edge algorithm cycle through the IoT topology to obtain the data upload speed. In order to improve the evaluation efficiency of sports events, the process of edge algorithm is designed. The contribution rate of evaluation index is calculated, and the consistency, minimum deviation, and minimum difference of the results are taken as the standard to design the evaluation method of sports events. In order to verify the performance of the optimized edge algorithm, the test data set and test platform are set up and the comparative experiment is designed. Compared with the traditional methods, the edge algorithm based on DSLL has lower computational cost, shorter computational time, higher evaluation accuracy, and more practical results.
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Sai Prasanthi, K., and K. V.Daya Sagar. "Survey on secure protocols for data sharing through edge of cloud assisted internet of things." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 92. http://dx.doi.org/10.14419/ijet.v7i2.7.10267.
Повний текст джерелаАнотація:
Nowadays Internet of Things (IoT) is the trending topic where we go. IoT is included in almost every device surrounded by us where valuable information is shared over the network to store it in the cloud or to transfer as a message alert to an individual. IoT devices generate a huge amount of data but only caring information is required and for that analytics needs to be performed. Analytics are reaching outside of the traditional datacenter towards the edge, where the IoT data is generated. So, here in this paper, the importance of secure data sharing over a network, generated by IoT devices is described and along with that the data flow between IoT and edge server is discussed, and the requirement of edge analytics is focused.
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Liu, Fagui, Zhenxi Huang, and Liangming Wang. "Energy-Efficient Collaborative Task Computation Offloading in Cloud-Assisted Edge Computing for IoT Sensors." Sensors 19, no. 5 (March 4, 2019): 1105. http://dx.doi.org/10.3390/s19051105.
Повний текст джерелаАнотація:
As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.
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Liu, Linyuan, Haibin Zhu, Shenglei Chen, and Zhiqiu Huang. "Privacy-Aware Task Assignment for IoT Audit Applications on Collaborative Edge Devices." Security and Communication Networks 2022 (June 21, 2022): 1–15. http://dx.doi.org/10.1155/2022/1336094.
Повний текст джерелаАнотація:
To meet the rapidly increasing demand for Internet of Things (IoT) applications, edge computing, as a novel computing paradigm, can combine devices at the edge of the network to collaboratively provide computing resources for IoT applications. However, the dynamic, heterogeneous, distributed, and resource-constrained nature of the edge computing paradigm also brings some problems, such as more serious privacy leakages and performance bottlenecks. Therefore, how to ensure that the resource requirements of the application are satisfied, while enhancing the protection of user privacy as much as possible, is a challenge for the task assignment of IoT applications. Aiming to address this challenge, we propose a privacy-aware IoT task assignment approach at the edge of the network. Firstly, we model the resource and privacy requirements for IoT applications and evaluate the resource satisfaction and privacy compatibility between edge devices and tasks. Secondly, we formulate the problem of privacy-aware IoT task assignment on edge devices (PITAE) and develop two solutions to the PITAE problem based on the greedy search algorithm and the Kuhn–Munkres (KM) algorithm. Finally, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the PITAE problem can be solved effectively and efficiently.
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Byun, Siwoo. "A Sensor-aware Offloading Model for IoT Edge Computing." Webology 19, no. 1 (January 20, 2022): 4505–14. http://dx.doi.org/10.14704/web/v19i1/web19297.
Повний текст джерелаАнотація:
In IoT sensor network environment, offloading is an important factor that affects all design objectives. Since massive amounts of data are collected every second to the gateway and so immediate processing is difficult, offloading is critical to quickly eliminate worthless data in advance. Similar sensor data are continuously generated except in abnormal situations such as sudden changes and failure events. Therefore, the amount of data processing and frequency of data transmission can be greatly reduced by classifying, filtering, and compressing the data. In addition, more meaningful IoT context can be analyzed by combining multiple sensor data, since the sensor values generated by each sensor has its own characteristics. The previous offloading techniques mainly focused on minimizing latency without using data context and data resizing. Therefore, a new filtering technique is required to enhance the offloading efficiency through precision control using sensor context patterns. This paper proposes a new sensor-aware context offloading model called SCOM to support efficient data filtering services for the edge-based IoT environment. The architecture of SCOM consists of three layers of sensor context, pattern context and transmission context. SCOM exploits context-aware stream pattern matching using general string matching based on slide window for sensor stream offloading. Experiments show that the performance gain of SCOM reaches to 14.8% with respect to the operation throughput. Since proposed data layering and pattern-based offloading scheme can improve the sensor data filtering performance in edge gateways, it can be used for IoT sensor monitoring applications.