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Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments
Praveen Borra1, Mahidhar Mullapudi2, Harshavardhan Nerella3, Lalithkumar Prakashchand4
1Praveen Borra, Department of Computer Science, Florida Atlantic University, Boca Raton, USA.
2Mahidhar Mullapudi, Senior Software Engineer, Microsoft, Redmond, USA.
3Harshavardhan Nerella, Senior Cloud Engineer, Mass Mutual, Austin, USA.
4Lalithkumar Prakashchand, Software Engineer, Meta Platforms, USA.
Manuscript received on 06 July 2024 | Revised Manuscript received on 16 July 2024 | Manuscript Accepted on 15 August 2024 | Manuscript published on 30 August 2024 | PP: 8-12 | Volume-13 Issue-6, August 2024 | Retrieval Number: 100.1/ijeat.F451913060824 | DOI: 10.35940/ijeat.F4519.13060824
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper examines the integration of Internet of Things (IoT) deployments with edge computing, with a focus 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 various industries, and outlines future research directions. IoT and edge computing revolutionise data processing by enabling real-time analytics, reducing latency, and enhancing operational efficiency. IoT involves interconnected devices that autonomously gather and exchange data, while edge computing processes data near its source, decentralising data processing and minimising data transmission to centralised 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. Centralised cloud architectures often struggle with the vast amounts of IoT data. Edge computing decentralises data processing, thereby reducing latency, enhancing real-time capabilities, and minimising bandwidth requirements. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass enables local data processing and machine learning at the edge, which is vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without the need for server management, which is 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, continually innovating to meet the dynamic demands of IoT deployment.
Keywords: Internet of Things (IoT), Edge Computing, AWS IoT, AWS Greengrass, Edge Devices, IoT Security, Edge Analytics, IoT Applications, Cloud Computing, Machine Learning at the Edge.
Scope of the Article: Computer Science and Applications