On-Premise AI refers to the deployment and operation of artificial intelligence (AI) systems within an organization’s own physical infrastructure, typically located on the premises of the organization itself. In contrast to cloud-based AI solutions, where AI services are hosted and accessed over the internet by third-party providers, on-premise AI involves hosting and managing AI resources locally, within the organization’s own data centers or servers. On-premise AI solutions are often chosen by organizations with specific requirements related to data control, compliance, and customization. However, the decision between on-premise and cloud-based AI depends on factors such as organizational priorities, IT infrastructure, budget considerations, and the nature of AI applications being deployed.
We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.
We review the distributed-fiber-sensing field trial results over deployed telecom networks. With local AI processing, real-time detection, and localization of abnormal events with cable damage threat assessment are realized for cable self-protection.
We report the distributed-fiber-sensing field trial results over a 5G-transport-network. A standard communication fiber is used with real-time AI processing for cable self-protection, cable-cut threat assessment and road traffic monitoring in a long-term continuous test.