An Integrated Framework for Real-Time Analysis and Observability of Wireless Sensor Data Using AWS Edge Service Capabilities

Wireless sensor networks generate vast data offering insightful information for applications from industrial monitoring to smart cities. Optimizing energy consumption in these networks is critical for increasing the efficiency and lifespan of these networks. Earlier cloud computing methodology usually faces challenges in bandwidth, latency and reliability. This research work identifies the current gap in the industry and adds edge computing in wireless sensor networks in processing huge data using AWS IoT Greengrass and explores the novel use case of visualizing AWS sensor data with AWS Quick Sight. The implementation of edge computing will reduce network congestion and lessen costs related to infrastructure. This is a key factor which not only improves the performance of IoT applications but also helps in scalability of cloud -based infrastructure. Also, it's imperative that most IoT applications deal with time series data and latency is a pivotal factor. In addition to the proposal of edge computing this work also incorporated a comparative study of edge computing and traditional cloud computing highlighting better latency, efficiency, reliability and scalability.