The difference that edge computing can make to your network of sensors or monitors
The incorporation of Acoem AI into your monitoring network opens the system up to greater interconnectivity and distributed intelligence. We have extended what we do at a single sensor level to correlate the data between different machines/sensors at any one time.
Acoem’s AI edge computing capabilities transform the way data is handled, processed, and delivered across networks of devices and monitoring systems around the world. Most IoT devices like those developed by Acoem for predictive maintenance and condition monitoring, as well as acoustic management, generate enormous amounts of data during the course of their operations. With our AI powered sensor networks, algorithms take into account the context not just the measurement and reduce the amount of data required to go to a centralised repository without losing information.
With the signal recognition and data processing done at the source within a network, you no longer need to fully rely on transmission and storage on the cloud or at a data centre. It reduces the amount of superfluous data being transmitted to servers and also cuts the cost of bandwidth for large amounts of data traveling over long distances. Your monitoring data, especially real-time data, will not suffer latency issues that can affect an application’s performance. By processing your data within your device or hardware, only the relevant data — based on the parameters you have established — will be sent back through the cloud, or the cloud can send data back to the edge device in the case of real-time application needs.
When operating an Acoem Acoustic Threat Detection network across your city, you may have hundreds or even thousands of individual sensors and connected cameras in use at any one time, dispersed over a large geographic area. Instead of massive quantities of data being fed to your central command centre’s servers, each ATD unit will have its own AI parameters embedded into the sensor, that will be configured to ignore standard motion detection or sounds, like car horns, voices, traffic signals, car alarms etc. Alerts would be triggered and data automatically transmitted for irregularities like explosions, gunshots, sirens or broken glass and changes in motion like crowds, seismic movement etc. Cameras could also communicate with each other for added insight during emergency situations.
