Which technology is primarily focused on reducing latency by processing data near its generation source?

Edge computing is primarily focused on reducing latency by processing data near its generation source. This technology involves placing computational resources closer to the data-generating devices, often at the "edge" of the network, rather than relying solely on centralized cloud servers. By processing data locally, edge computing minimizes the time it takes to send information back and forth to a remote server, significantly improving response times and ensuring that applications can function efficiently even with high data velocity.

For instance, in scenarios requiring real-time analytics, such as in autonomous vehicles or industrial automation, the ability to quickly process data collected from sensors can be critical for timely decision-making. By avoiding the delays associated with long-distance data transmission, edge computing enhances overall system performance, reduces bandwidth utilization, and can improve privacy and security by keeping sensitive data closer to its source.

This is in contrast to cloud storage, which typically involves data being sent to a centralized location for processing and storage, making it less effective in scenarios where low latency is a critical factor. IoT devices generate data but do not specifically focus on reducing latency themselves; rather, they often rely on edge computing or cloud solutions for processing. Data analytics is a broader category that encompasses the analysis of data after it has been collected, without inherently addressing