Enhancing IoT Data Processing and Streaming at the Edge: A Comparative Analysis of AWS Greengrass Architectures with Message Brokering and Stream Management
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P102Keywords:
AWS Greengrass, Edge Computing, IoT Applications, Hybrid Architecture, Stream Management, Message Brokering, Latency Optimization, Scalability, Data Ingestion, Security EnhancementsAbstract
The Internet of Things (IoT) has revolutionized various industries by enabling the connection and communication of billions of devices. However, the increasing volume and velocity of IoT data pose significant challenges in terms of data processing and management. Edge computing, which brings computation and data storage closer to the source of data, offers a promising solution to these challenges. AWS Greengrass, a key edge computing service provided by Amazon Web Services (AWS), facilitates local processing and communication of IoT devices. This paper presents a comprehensive comparative analysis of AWS Greengrass architectures, focusing on message brokering and stream management. We evaluate the performance, scalability, and efficiency of different configurations of AWS Greengrass, including the use of MQTT brokers and stream managers. The results provide insights into the optimal deployment strategies for enhancing IoT data processing and streaming at the edge
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