Description: Temporal scalability in the context of Edge AI refers to the ability of a system to handle increasing amounts of data generated and processed over time. This means that the system must not only be capable of processing real-time data but also adapt to an increase in the volume of data as it is generated. Temporal scalability is crucial in applications where data flows continuously, such as surveillance, health monitoring, and traffic management. A system with this capability can dynamically adjust its processing and storage capacity, ensuring that latency remains low and decisions are made quickly. This capability is especially relevant in Edge AI environments, where local devices process data close to the source of generation, minimizing the need to send large volumes of data to the cloud. Temporal scalability allows these systems to handle data spikes efficiently while optimizing resource usage, resulting in more effective operation. In summary, temporal scalability is an essential component to ensure that Edge AI systems can operate effectively in an increasingly interconnected and data-driven world.
