The Edge Power
Data Analytics and AI
The transfer from the Internet of Things (IoT) to Systems of Systems was mainly driven by the energy cost of data communication compared with distributed, dynamic and flexible computing at the edge, i.e. with embedded devices. Integrated by seamless and trusted connectivity (e.g. 5G) it will allow the value-based exchange of data. This will support societal needs related to the reduction of greenhouse gases, efficient resource utilization or disease prediction while at the same guaranteeing citizens’ privacy.
- Embedded devices for secure end-2-end communication based on hardware security
- Cloud-based data analytics platform (based on AWS Analytics) available as SaaS model.
- edge Gateway with 5G connectivity with optional integrated edge AI
- IoT device management platform for remote IoT device and access management
- IoT solutions that connect, monitor, and control 24/7/365 your environment to produce innovative and differentiated values
- Wider business horizon due secure sharing of data throughout your (IoT) value chain
- More stable, reliable, scalable, open, secure, evolvable and globally affordable IoT solution
- Predict and identify system anomalies much faster and reduce system downtimes
- Apply virtual assistants and increase the reliability of your processes (reduced risks in case of COVID-19 measures)
Artificial intelligence (AI) has not only become accepted in our daily life (with cloud-based advanced assistant systems like Alexa, Siri, Google Home, etc.) but also in the industry by transforming data into information to get a better understanding of their problems e.g. during production and identify or even predict system errors much faster. Cross-functional data alignment from sensor and tool data along the value chain improves the manufacturing processes through a rich, multivariate data set.
AI systems improve machine-to-machine learning, natural language processing and predictions about system behavior. Cross-functional data alignment from sensor and tool data along the value chain improves the manufacturing processes through a rich, multivariate data set. The tools can learn from prior designs and enhance their ability to detect failures over time. Overall, data analytics and AI support act-based knowledge, pattern recognition, and structured learning will reduce errors, streamline processes, and decrease costs.