Nokia Announces AI-enabled Far Edge Compute to Boost OT Responsiveness and Decision Making

Author photo: Chantal Polsonetti
ByChantal Polsonetti
Company and Product News

Nokia launched MX Grid, an on-premise, hyper interconnected and distributed AI/ML solution designed to enable organizations to improve OT responsiveness and decision making by processing and analyzing data closest to the source.  As part of the Nokia one platform, MX Grid is intended to accelerate Industry 4.0 adoption, building on the Nokia MX Industrial Edge (MXIE) platform and leveraging reliable wireless connectivity, to offer a new OT data processing architecture that facilitates IT/OT convergence, supporting mission- and life-critical industrial applications.

AI-enabled Far Edge

Today most AI/ML assets run in cloud environments. With the previously announced MXIE, Nokia introduced OT compliant AI/ML processing capabilities on-premises, whereas MX Grid brings this capability even closer to the OT data source. MX Grid leverages a pool of orchestrated compute capable field devices – known as ‘micro-edges’ – with a specialized, AI-capable software stack. These micro-edges are connected by private wireless networks and/or reliable Wi-Fi using MX Boost, which augments both technologies.

\MX Grid is designed to enhance the efficiency of enterprise OT operations using decentralized workload processing and real-time, agile decision-making, bringing intelligence to legacy OT assets. The solution enables a more effective implementation of a wide range of mission- and life-critical industrial applications, including predictive maintenance, security and surveillance, worker safety, tracking and positioning, and quality assurance.

For example, in a quality assurance use case the application on the micro-edge analyzes real-time sensor data and video feeds coming from the connected machine. Depending on the deviation level, either an immediate action can be triggered directly by the micro-edge or the MXIE peer application takes over the monitoring for deeper analysis of real time data for later corrective action. This results in improved latency and optimized network load.

The ability to integrate and utilize connected worker data and situational sensory information within MX Grid brings new capabilities for worker safety use cases. For example, the Visual Position and Object Detection (VPOD) application utilizes the MX Grid architecture to process video data next to the camera, together with MXIE, for more accurate asset tracking and positioning. This is intended to significantly improves worker safety and contextual awareness, when combined with other OT data sources.

Further information on Industrial AI's Role in Digital Transformation of Manufacturing Industries is available here.  

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