IoT is the driving force that would redefine Industry 4.0. By integrating IoT, with artificial Intelligence, thus forming a culmination known as AIoT, the reformation amongst businesses will be redefined. Back in 2017 ,a report by Gartner states that by the year 2022, more than 80% of the enterprises would deploy AI with IoT.
The AIoT provides a platform for the improved business model, enhanced efficiency, meet growing demand, and for instilling better customer experiences. While IoT is tagged as the spinal cord of all operations in AIoT, experts have identified AI to be the brain of IoT, thus controlling all operations.
How does AIoT work?
A report by Juniper Research states that the IoT connected devices would triple to almost 38 billion in 2020.
Integration of AI in IoT, adds another capability of acting and rectifying the loophole or gaps within the system. AI scrutinizes the patterns with the telemetry data, identifies the gaps that is hindering in the process of automation and then starts initiating action, thus self-healing in the process.
Automobile Industry with AIoT
The hype of smart cars uses technologies such as IoT, where sensors are embedded for collecting data, storing it, and connecting it with the various devices. Augmenting this telemetry data with AIoT, assists in accessing the data, that not only renders the smart cars to identify the blockages, road patterns or internal issue but also to respond by self-controlling the operations of the car, maintaining the car speed and changing the direction of the car, as and when demanded. A report by Mckinsey Insight states that, by the year 2030, 15% of cars will be autonomous and would be utilizing technologies such as IoT.
"IoT will provide the massive amount of data that AI needs for learning and transforming that data into meaningful, real-time insights on which IoT devices can act faster" , writes Money Garg, Co-Founder of Readymotive startup which recently built world's cheapest AIoT powered fuel telematic device for four-wheelers vehicle.
There are many challenges while deploying AIoT in practice. For instance,
• The edge computing issues for solving the latency of transmitting huge IoT data in networks and achieving real-time responses with high-performance AI for analyzing the huge data.
• Security issue is another crucial challenge to provide safety and privacy in AIoT applications.
To this, Shubham, VP of Technology of Readymotive adds, "As developers on an average see between 15 and 50 errors per 1,000 lines of code, we at Readymotive are working on implementing three-layered code security practice and also looking to partner with third party security tech firms in order to keep our customers data safe and secure, in every possible way".