Insights Into IoT Data and an Innovative DWT-Based Technique to Denoise Sensor Signals

Abstract

The Internet of Things (IoT) has been widely discussed and investigated, not only by academics but also by various segments of industry and commerce. Once established and fully operational, this technology is set to offer considerable benefits. Sensors will send large amounts of data for analysis and interpretation in real-time or near real-time. These data streams will flow at high speed, in large volumes, varying in type, and value. However, it is likely that sensors will face numerous obstacles what can pollute their data with significant amount of noise. This paper investigates sensor data streams and sensor signals in order to identify their specific characteristics and issues, particularly the problem of noise. This paper also investigates discrete wavelet transform (DWT) to reduce noise. Results here show significant improvements in the sensor signal when using DWT. The principal conclusion of our analysis is that, by knowing the main issues of IoT data, it is possible to track problems and reduce data errors. Another practical conclusion is that DWT is a powerful tool for reducing signal noise.