Sensors have now become pervasive across almost all market segments, from wearable devices which utilise them to monitor users’ movements and vital signs, to sensors attached to industrial machines for Condition Based Monitoring (CbM) to give early warning of wear or failure helping in the drive towards Industry 4.0.
The latest generation of MEMS sensors bring ever increasing levels of reliability and measurement accuracy enabling even deeper insight for the applications which employ them. As this more detailed sensor data increases, the need to process it effectively so it can be interpreted correctly becomes ever more important.
Devices with onboard sensors are now networked and connected to the IoT allowing the data they gather to be processed in the cloud and interpreted and acted on efficiently.
To reduce some of the limitations associated with cloud processing, such as network availability and bandwidth, and to further maximise speed and efficiency some of this data processing can be carried out locally by ‘Edge’ computing devices. Edge computing helps to increase data privacy, reduce latency and lightens the load on the cloud servers and associated bandwidth.
These Edge devices can also employ Artificial Intelligence (AI) and Machine Learning to further improve performance particularly for time-sensitive applications. For example, in a wearable device measuring a user’s vital signs, it is important to be able to provide a real-time interpretation of the sensor data, whereas in an air quality monitoring device some latency can be tolerated due to the intermittent sampling rates. However, there are also some disadvantages with Edge computing, it requires additional equipment in the form of hubs or router devices to be installed locally further adding cost and complexity.
#Condition Based Monitoring #Artificial Intelligence #onboard sensors #Edge computing #Machine Learning #Digital Semiconductors Pvt. Ltd.
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