Photo by Dynamic Wang on Unsplash

According to a report published recently by globenewswire, the global AIoT market will reach $65.9B by 2025, growing at 39.1% CAGR and the global market for IoT data as service solutions will reach $8.2B USD by 2025.

The main reasons for AIOT to become mainstream and popular is mainly driven by requirements of latency and velocity of decision making in the field by the end devices, cost of moving the sensor data to the cloud for processing, the regulatory/proprietary restrictions on the movement of data from the scene of action.

This along with advances in well optimized AI and ML algorithms have made it possible to address the above issues by making the IOT deployments operate ‘offline’(intermittent connectivity), infer the data locally, and securely communicate with cloud the relevant outcomes instead of dumping raw streams of data as earlier.

Role of AIOT can be appreciated in the scenario constrained, battery operated devices. There is a cost to send data to the cloud both in terms of telco/RF service provider and battery consumption. In some cases, cost of replacing the battery is more than the cost of device. Therefore, it makes sense to have the inference engine right on the device to send only meaningful outcomes instead of sending the raw stream of data. This helps in better utilization of resources and extended device life. Typical applications are Industrial monitoring, logistics, medical, agriculture etc.

These advances in AIOT will open yet another business and operational model generation AI Decision as a Service (AIDaaS).




Innovator |IoT Product {Conception, Management}|ML/AI on the Edge | Image Processing| Audio| Semiconductors| LinkedIn:

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Amit Gupta

Amit Gupta

Innovator |IoT Product {Conception, Management}|ML/AI on the Edge | Image Processing| Audio| Semiconductors| LinkedIn:

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