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Journal articles on the topic 'Edge IoT'

Author: Grafiati

Published: 16 November 2024

Last updated: 25 July 2025

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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 (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 app

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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.

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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 syste

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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 (2020): 3337. http://dx.doi.org/10.3390/s20123337.

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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

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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 (2021): 2088. http://dx.doi.org/10.3390/electronics10172088.

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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

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Riane, Driss, Widad Ettazi, and Mahmoud Nassar. "An Allele Based-Approach for Internet of Transactional Things Service Placement in Intelligent Edge Environments." IoT 5, no. 4 (2024): 785–800. http://dx.doi.org/10.3390/iot5040035.

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The rapid expansion of the Internet of Things (IoT) has steered in a new generation of connectivity and data-driven decision-making across diverse industrial sectors. As IoT deployments continue to expand, the need for robust and reliable data management systems at the network’s edge becomes increasingly critical, especially for time-sensitive IoT applications requiring real-time responses. This study delves into the emerging research area known as the Internet of Transactional Things (Io2T) at the edge architecture, where the integration of transactional ACID properties into IoT devices and o

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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 architectur

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Sai, Sandeep Ogety. "Enhancing Cloud Security Governance with AI and Data Analytics." European Journal of Advances in Engineering and Technology 8, no. 7 (2024): 132–42. https://doi.org/10.5281/zenodo.14274546.

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The group of real-world physical devices like sensors, machines, vehicles and various “things” connected to Internet is called as Internet of things (IoT). The major challenge in IoT is that  it is fully dependent on the cloud for all kinds of computation, which leads to high latency in the IoT devices. To overcome this latency issue, the Serverless edge computing and AI approaches were introduced newline. Serverless edge computing allows moving the data goverence and managing closer to the Serverless edge of the device. ICT’s three pillars namely computing, network and

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Praveen, Borra. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology (IJEAT) 13, no. 6 (2024): 8–12. https://doi.org/10.35940/ijeat.F4519.13060824.

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<strong>Abstract:</strong> 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 exchangin

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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 (2021): 17–25. http://dx.doi.org/10.5383/juspn.15.01.003.

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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 i

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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 (2021): 1460. http://dx.doi.org/10.3390/electronics10121460.

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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 c

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Bansal, Malti, and Harshit. "IoT based Edge Computing." December 2020 2, no. 4 (2021): 204–10. http://dx.doi.org/10.36548/jtcsst.2020.4.005.

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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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Azad, Tanzima, M. A. Hakim Newton, Jarrod Trevathan, and Abdul Sattar. "IoT edge network interoperability." Computer Communications 236 (April 2025): 108125. https://doi.org/10.1016/j.comcom.2025.108125.

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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 (2024): 8–12. http://dx.doi.org/10.35940/ijeat.f4519.13060824.

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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 computin

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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 (2024): 18–23. http://dx.doi.org/10.37547/ajast/volume04issue03-04.

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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 exist

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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 (2020): 2294. http://dx.doi.org/10.3390/s20082294.

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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. B

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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 (2023): 1–7. http://dx.doi.org/10.46610/aibtia.2023.v02i03.001.

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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

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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.

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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 tech

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Mahalingam, Anandaraj, Ganeshkumar Perumal, Gopalakrishnan Subburayalu, et al. "ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks." Sensors 23, no. 19 (2023): 8044. http://dx.doi.org/10.3390/s23198044.

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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, thi

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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 (2021): 140. http://dx.doi.org/10.3390/app12010140.

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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 Ed

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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 (2023): 8282. http://dx.doi.org/10.3390/s23198282.

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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 th

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Ciuffoletti, Augusto. "Stateless IoT." Information 11, no. 2 (2020): 85. http://dx.doi.org/10.3390/info11020085.

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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 functio

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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 Io

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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 (2022): 1–25. http://dx.doi.org/10.1145/3453171.

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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 au

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Wankhade, Shubham D., and Prof. Snehal V. Raut. "Conversions of IoT, Edge and Cloud Computing." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 468–73. https://doi.org/10.5281/zenodo.14192347.

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Over the past few years, the idea of edge computing has seen substantial expansion in both academic and industrial circles. This computing approach has garnered attention due to its integrating role in advancing various state-of-the-art technologies such as Internet of Things (IoT), 5G, artificial intelligence, and augmented reality. In this chapter, we introduce computing paradigms for IoT, offering an overview of the current cutting-edge computing approaches that can be used with IoT. Furthermore, we go deeper into edge computing paradigms, specifically focusing on cloudlet and mobile edge c

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Li, Borui, Wei Dong, Gaoyang Guan, et al. "Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads." ACM Transactions on Sensor Networks 17, no. 3 (2021): 1–23. http://dx.doi.org/10.1145/3442363.

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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 offl

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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.

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Suryadi, Dikky, Cut Susan Octiva, T. Irfan Fajri, Uli Wildan Nuryanto, and Muhammad Lukman Hakim. "Optimasi Kinerja Sistem IoT Menggunakan Teknik Edge Computing." Jurnal Minfo Polgan 13, no. 2 (2024): 1456–61. https://doi.org/10.33395/jmp.v13i2.14102.

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Internet of Things (IoT) telah menjadi salah satu teknologi kunci dalam menghubungkan berbagai perangkat untuk mendukung otomatisasi dan efisiensi di berbagai sektor. Namun, pertumbuhan eksponensial perangkat IoT juga menimbulkan tantangan besar dalam hal keterbatasan bandwidth, latensi, dan konsumsi daya yang tinggi pada jaringan. Teknik Edge Computing muncul sebagai solusi potensial untuk mengatasi tantangan ini dengan mendekatkan proses komputasi ke lokasi sumber data, mengurangi beban pada cloud, dan meningkatkan respon waktu. Penelitian ini bertujuan untuk mengoptimalkan kinerja sistem Io

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Zhang, Jinnan, Changqi Lu, Gang Cheng, et al. "A Blockchain-Based Trusted Edge Platform in Edge Computing Environment." Sensors 21, no. 6 (2021): 2126. http://dx.doi.org/10.3390/s21062126.

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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

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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 (2023): 9033–42. http://dx.doi.org/10.15680/ijirset.2023.1206160.

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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 sy

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Byun, Siwoo. "Replicated Data Management Using Scaled Segment Chain in Unstable IoT Environments." Webology 19, no. 1 (2022): 4286–98. http://dx.doi.org/10.14704/web/v19i1/web19282.

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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

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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 (2021): 62. http://dx.doi.org/10.3390/jsan10040062.

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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

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Petri, Ioan, Omer Rana, Yacine Rezgui, and Fodil Fadli. "Edge HVAC Analytics." Energies 14, no. 17 (2021): 5464. http://dx.doi.org/10.3390/en14175464.

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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 ed

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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 (2021): 2156. http://dx.doi.org/10.3390/electronics10172156.

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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

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Bhargavi, Bhavesh Dalal, and Chaugule Archana. "Colour Monitoring of Plant using IoT by Edge IT." Recent Trends in Cloud Computing and Web Engineering 2, no. 3 (2021): 1–9. https://doi.org/10.5281/zenodo.4450036.

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<em>As the English poet William Wordsworth once thought: "Your mind is the garden, your thoughts are the seeds, and the harvest can be either flowers or weeds. Keeping your plants alive can be quite the challenge as they are very bad at communication. "Organic farming is a preparation which avoids or largely ignores the use of mock inputs (such as fertilisers, pesticides, hormones, feed, etc.) WSN can be used in various areas such as intensive care of the plant, wireless measurements of plant moisture and temperature, controlling nutrition of plant based on colour coding system, etc.

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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 (2021): 155014772110353. http://dx.doi.org/10.1177/15501477211035332.

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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 de

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Dharam, Gami *. Asst. Prof. Dhaval Nimavat Asst. Prof. Shubham Sharma. "EDGE TECHNOLOGIES IN IoT AND APPLICATION SCENARIO OF RFID BASED IoT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 838–42. https://doi.org/10.5281/zenodo.56026.

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Internet of Things possesses the power to change the era. IoT will offer an advance connectivity between objects which will change the face of machine-to-machine communication. IoT will connect autonomous systems, devices and heterogeneous machines and make them communicate without human interactions. Many technologies will play significant role in IoT implementation. In this paper, we aim to describe the candidate of edge technologies in IoT and demonstrate how RFID based IoT system will look like with an example of real time application scenario. We show the functional level IoT architecture

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Chinta, Swetha. "Edge AI for Real-Time Decision Making in IOT Networks." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 09 (2024): 11293–309. https://doi.org/10.15680/ijircce.2024.1209044.

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The proliferation of Internet of Things (IoT) devices and networks has led to an exponential increase in data generation at the network edge. Processing this data in real-time to enable rapid decision making presents significant challenges for traditional cloud-centric architectures. Edge AI, which involves deploying artificial intelligence algorithms directly on edge devices and gateways, has emerged as a promising solution to enable lowlatency analytics and decision making in IoT networks. This paper presents a comprehensive review and analysis of Edge AI techniques for real-time decision ma

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Dauda, Abdulkadir, Olivier Flauzac, and Florent Nolot. "A Survey on IoT Application Architectures." Sensors 24, no. 16 (2024): 5320. http://dx.doi.org/10.3390/s24165320.

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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 capabilit

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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 rece

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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 (2019): 2078. http://dx.doi.org/10.3390/s19092078.

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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 transmiss

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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 (2019): 3. http://dx.doi.org/10.3390/sym12010003.

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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

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Agustini, Sherly. "Kajian Literatur: Perkembangan Edge Computing dalam Mendukung IoT." Engineering and Technology International Journal 7, no. 01 (2025): 36–43. https://doi.org/10.55642/eatij.v7i01.969.

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Internet of Things (IoT) telah berkembang pesat dalam beberapa tahun terakhir, memberikan dampak signifikan di berbagai sektor seperti manufaktur, kesehatan, transportasi, dan smart city. Meskipun IoT menawarkan kemajuan besar dalam otomatisasi dan efisiensi, implementasinya menghadapi tantangan besar terkait latensi, konsumsi bandwidth yang tinggi, dan masalah keamanan data. Salah satu solusi yang dianggap mampu mengatasi tantangan ini adalah Edge Computing, sebuah paradigma komputasi terdistribusi yang memindahkan pemrosesan data lebih dekat ke perangkat edge untuk meningkatkan efisiensi, me

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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 (2023): 649. http://dx.doi.org/10.3390/electronics12030649.

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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 e

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Muñoz, Raul, Ricard Vilalta, Noboru Yoshikane, et al. "Integration of IoT, Transport SDN and Edge/Cloud computing for Dynamic Distribution of IoT Analytics and Efficient Use of Network Resources." IEEE Journal of lightwave Technologies 36, no. 7 (2018): 1420–28. https://doi.org/10.1109/JLT.2018.2800660.

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Internet of Things (IoT) requires cloud infrastructures for data analysis (e.g., temperature monitoring, energy consumption measurement, etc.). Traditionally, cloud services have been implemented in large datacenters in the core network. Core cloud offers high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, collecting information and bringing it into one core cloud infrastructure is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A scalable and e

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Huang, Hongyang, Mohammed Dauwed, Morched Derbali, et al. "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.

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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 cos

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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 (2020): 1–18. http://dx.doi.org/10.33166/aetic.2020.05.001.

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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 recentl

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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

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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 (2024): 608–33. http://dx.doi.org/10.3390/iot5040027.

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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

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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 (2024): 335–42. http://dx.doi.org/10.22214/ijraset.2024.64517.

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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 p

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Mehmood, M. Yasir, Ammar Oad, Muhammad Abrar, et al. "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.

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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 direc

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