Edgefusing

Edgefusing is a term used in computing and data processing to describe the process of integrating data from distributed edge devices or locations with data residing in a central cloud or data center. This integration aims to leverage the strengths of both edge and cloud environments, enabling more efficient processing, real-time analytics, and improved decision-making. Edge devices, such as IoT sensors, mobile phones, or local servers, collect vast amounts of data. Fusing this data at the edge, or with data flowing to the cloud, allows for immediate analysis and action without the latency associated with sending all raw data to a central location.

The primary goals of edgefusing include reducing data transmission costs, improving response times for critical applications, and enhancing data security by processing sensitive information closer to its source. By intelligently combining edge-generated data with historical or more comprehensive datasets in the cloud, organizations can create richer insights and more accurate models. This can involve techniques like data aggregation, filtering, anomaly detection, and machine learning model deployment at the edge, with results then fused with cloud data for further analysis or long-term storage. Edgefusing is a key component of modern edge computing architectures, facilitating the development of intelligent applications across various industries.