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Relevant bibliographies by topics / AWS IoT Greengrass

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Academic literature on the topic 'AWS IoT Greengrass'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "AWS IoT Greengrass"

1

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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2

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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3

Choi, Hoan-Suk, Seung-Ho Jang, Hyo-Rim Cheon, Min-Jeong Kim, and Woo-Seop Rhee. "Development of a Single-Person Care IoT service using AWS Greengrass and SageMaker." Journal of Digital Contents Society 24, no. 12 (2023): 3121–30. http://dx.doi.org/10.9728/dcs.2023.24.12.3121.

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Sri, Harsha Vardhan Sanne. "Overcoming Network Bottlenecks and Latency Issues in Distributed AWS Architectures." Journal of Scientific and Engineering Research 7, no. 9 (2020): 223–29. https://doi.org/10.5281/zenodo.12787485.

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Distributed AWS architectures offer unparalleled scalability, flexibility, and resilience, making them pivotal in modern cloud computing. However, they also present significant challenges, particularly network bottlenecks and latency issues. These problems can degrade system performance, escalate operational costs, and negatively impact user experience. This paper explores comprehensive strategies to overcome these challenges, ensuring the efficient and reliable operation of AWS architectures. Key strategies include effective load balancing, network segmentation, data transfer optimization usi

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Krishnamurthi, Rajalakshmi, Raghav Maheshwari, and Rishabh Gulati. "Colorizing and Captioning Images Using Deep Learning Models and Deploying Them Via IoT Deployment Tools." International Journal of Information Retrieval Research 10, no. 4 (2020): 35–50. http://dx.doi.org/10.4018/ijirr.2020100103.

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Neural networks and IoT are some top fields of research in computer science nowadays. Inspired by this, this article works on using and creating an efficient neural networks model for colorizing images and transports them to remote systems through IoT deployment tools. This article develops two models, Alpha and Beta, for the colorization of the greyscale images. Efficient models are developed to lessen the loss rate to around 0.005. Further, it also develops an efficient model for the captioning of an image. The paper then describes the use of tools like AWS Greengrass and Docker for the depl

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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 (2020): 4102. http://dx.doi.org/10.3390/app10124102.

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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

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7

Leclerc, Maxime. "Enhancing IoT Data Processing and Streaming at the Edge: A Comparative Analysis of AWS Greengrass Architectures with Message Brokering and Stream Management." International Journal of AI, BigData, Computational and Management Studies 3 (2022): 8–20. https://doi.org/10.63282/3050-9416.ijaibdcms-v3i1p102.

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István, Pelle, Paolucci Francesco, Sonkoly Balázs, and Cugini Filippo. "P4-assisted seamless migration of serverless applications towards the edge continuum." Future Generation Computer Systems 146 (April 19, 2023): 122–38. https://doi.org/10.1016/j.future.2023.04.010.

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Serverless computing has recently been presented as an effective technology for handling short-lived compute tasks in the cloud. It has the potential of becoming an attractive option also in the context of edge computing where resource-aware deployment, constrained by both limited edge computing resources and experienced latency, plays a vital role. In this paper, we present and experimentally validate a framework that oversees serverless applications in an edge computing scenario. It completely automates serverless application deployment and provides hitless dynamic migration of application c

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Dissertations / Theses on the topic "AWS IoT Greengrass"

1

Wheeler, Nathan. "On the Effectiveness of an IOT - FOG - CLOUD Architecture for a real-world application." UNF Digital Commons, 2018. https://digitalcommons.unf.edu/etd/855.

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Fog Computing is an emerging computing paradigm that shifts certain processing closer to the Edge of a network, generally within one network hop, where latency is minimized, and results can be obtained the quickest. However, not a lot of research has been done on the effectiveness of Fog in real-world applications. The aim of this research is to show the effectiveness of the Fog Computing paradigm as the middle layer in a 3-tier architecture between the Internet of Things (IoT) and the Cloud. Two applications were developed: one utilizing Fog in a 3-tier architecture and another application us

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Books on the topic "AWS IoT Greengrass"

1

Kurniawan, Agus. Learning AWS IoT: Effectively manage connected devices on the AWS cloud using services such as AWS Greengrass, AWS button, predictive analytics and machine learning. Packt Publishing, 2018.

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2

Mitra, Indraneel, and Ryan Burke. Intelligent Workloads at the Edge: Deliver Cyber-Physical Outcomes with Data and Machine Learning Using AWS IoT Greengrass. Packt Publishing, Limited, 2022.

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Book chapters on the topic "AWS IoT Greengrass"

1

Rehan, Syed. "Edge Development Using AWS IoT Greengrass." In AWS IoT With Edge ML and Cybersecurity. Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0011-5_6.

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