Taking Microsoft’s Azure Digital Twins to the next level in IoT
We’re taking Microsoft’s Azure Digital Twins to the next level of IoT
As countless IoT applications monitor live systems, real-time analytics play an important role in identifying problems or finding opportunities and then reacting quickly to make a difference. How can digital twins benefit from implementing real-time IoT analytics?
Consider a telematics software application that tracks a domestic truck to ensure on-time deliveries. Dispatchers receive telemetry from IoT-connected trucks every few seconds detailing their position, speed, lateral acceleration, engine parameters, and cargo viability. In a classic needle-and-shark scenario, dispatchers must continuously sift through telemetry from thousands of trucks to detect issues such as lost or tired drivers, engines requiring maintenance, or unreliable cargo cooling. They also need to act quickly to keep the supply chain running smoothly. Real-time analytics can help track these devices and tackle the seemingly impossible task of automatically scanning telemetry as it arrives, analyzing it for anomalies that require attention, and notifying dispatchers when conditions warrant.
While the software digital twin concept was originally developed for use in product lifecycle management and infrastructure, it also has the potential to greatly simplify the construction of applications that implement real-time analytics for the IoT. For example, a telematics application can use a digital twin for each truck to monitor the truck’s parameters (maintenance history, driver records, etc.) and its dynamic state (position, speed, engine status, and load, etc.). The digital twin can analyze telemetry from the truck and update that status information and generate alerts as needed. Real-time analytics code can incorporate machine learning techniques to quickly detect anomalies. Thousands of digital twins running simultaneously can monitor all trucks in a fleet and inform dispatchers while reducing their workload.