Filling in the gaps in IoT
The Internet of Things aka IoT is having a dramatic effect on many industries from healthcare to building management and the millions of points in between. Simply put IoT allows information to be collected from the physical world and physical devices to be controlled remotely.
But IoT is not simple! Connecting sensors to the internet in ways that are reliable and secure means that every type of device has to have differences. You can’t look at a device and understand what it does without context. And today most manufacturers of devices use their own “standards” which demand their own hubs or controllers.
A company that decides to use IOT will end up having to buy multiple devices from a multitude of different vendors and then using programmers to build methods of making it all work together. The results may well be impressive, but the code to make them work is going to be complex.
If you have ever worked in an office with meeting rooms that use touch panels to control the lights and presentation equipment, you will have experienced just how complex it can be. Have you had to wait for ten mins at the beginning of the meeting for the presenter to work out how to get his PC to display correctly on the screen, or for someone to work out how to close the blinds?
Now imagine how complex it can be to manage the sensors and actuators across a factory or warehouse, or at an airport or railway system? When things work it can be beautiful, but when things don’t work as expected it can take thousands of man hours of work to find the root cause and solve the problem. The challenge is that IoT devices are just like any other business transaction; monitoring at just the highest levels may feel good, but doesn’t provide the knowledge to solve problems quickly.
What is needed is a system that provides simple visibility of every message going through the IoT network. When a problem starts to happen, you want the ability to understand what lead up to the problem, allowing the situation to be quickly understood and action taken to solve an upcoming issue before it impacts the user experience. This is exactly what Nastel AutoPilot Insight provides, reducing MTTR, increasing MTBF; lowering costs and improving productivity and user experience.
Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:
- Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
- Raw information handling and analytics speed
- End-to-end business transaction tracking that spans technologies, tiers, and organizations
- Intuitive, easy-to-use data visualizations and dashboards
If you would like to learn more, click here