Concluding Remarks and Summary

Abstract

This book focused on sensor analysis for IoT applications. In Chapter 2, we introduced basic operational principles of select types of sensors, namely, accelerometer, magnetometer, gyroscope, and pressure sensors because those sensors are heavily used in IoT applications. We also addressed limitations and potential solutions that one may encounter when using these sensors in practical IoT devices. We described where sensors should be located and how that location affects results. We discussed magnetic interference, and how (in some cases) that can be compensated for mathematically. In Chapter 3, we described sensor fusion algorithms. It is often insufficient to fully describe a problem with a single sensor, as individual sensor types have their own limitations. With orientation estimation, Kalman filtering, and virtual gyroscope examples, we explained how sensor fusion can overcome the limitations and improve sensor operations. We also briefly described the mathematical background used for the sensor fusion algorithms. MATLAB scripts and software tools were also introduced to help the readers develop their own sensor fusion applications. In Chapter 4, we reviewed machine learning algorithms to help the readers understand fundamentals of analytics for sensor data. Example software tools also can help the readers practice machine learning algorithms and sensor data analytics. In Chapter 5, we described several domains of IoT applications with relevant software tools.