Understanding IoT battery failure prediction
On 5th December, a learning event was organized, and the topic was predicting the battery performance of IoT sensors that Manikant introduced. K. As the world is heading towards more digitization, the use of IoT has increased rapidly. It can incorporate anywhere, be it at the home, office, agriculture, manufacturing industry, etc. He gave an idea to the students about batteries and different types of sensors used in IoT devices.
Further, he explained different data types and how battery performance is direct with time. He talked about the top sensor types in IoT, such as accelerometer, proximity, IR, gas, temperature, and many more. Mr. Manikant showed the graph to help understand the battery life cycle and predict battery failures. Later, he discussed the various challenges in IoT, including a high volume of data, a variety of data, and different approaches to tackle them.
Mr. Manikant helped the student understand the base model of ARIMA (Auto regression integrated moving averages) that help make predictions. He also briefed how time series neural networks work, starting from capturing all dependent features, time dependency, dealing with complex non-linear data patterns, and new features extraction. The event concluded with questions where Mr. Manikant answered the students’ queries to grasp the concept.
#SCIT#MBA#DSDA#ITBM#IoT
