Internet of Things (IoT) analytics is the application of data analysis tools and procedures to realize value from the huge volumes of data generated by connected IoT devices. Data integration in a complex network of devices is becoming increasingly challenging for global enterprises. As the number of connected devices grows exponentially, companies are struggling to make sense of the plethora of data generated. The datasets are more complex, less structured and are generated in greater volumes than ever before.

ICURO team of network engineers has deployed an artificial intelligence (AI) driven baselining. Baselining is a method used to analyze network dynamics to extract behavioural patterns that help define what is the “normal” (baseline) behavior for that specific network. The actual network performance is then compared with that baseline. AI driven proactive insights determine global patterns and trends and deviations to provide system-generated insights. AI network maintenance analytics uses machine learning algorithms and automated workflows to perform logical troubleshooting steps, which enable an engineer to execute and resolve the issue. This helps the engineers detect issues and vulnerabilities, analyze the root causes, and quickly execute corrective actions. The network device operation log Information trains the reliability design and maintenance machine learning models.