Using Candlestick Charting and Dynamic Time Warping for Data Behavior Modeling and Trend Prediction for MWSN in IoT

Concepcion Sanchez Aleman, Niki Pissinou, Sheila Alemany, Georges A. Kamhoua

Abstract: There is a rapid emergence of new applications involving mobile wireless sensor networks (MWSN) in the field of Internet of Things (IoT). Although useful, MWSN still carry the restrictions of having limited memory, energy, and computational capacity. At the same time, the amount of data collected in the IoT is exponentially increasing. We propose Behavior-Based Trend Prediction (BBTP), a data abstraction and trend prediction technique, designed to address the limited memory constraint in addition to providing future trend predictions. Predictions made by BBTP can be employed by real-time decision-making applications and data monitoring. BBTP applies a candlestick charting technique to abstract the data behavior of a time partition in evolving data streams. It also quantifies differences between a pair of consecutive time partitions using dynamic time warping (DTW) at the sensor node. Then, it forwards the data to an Internet-enabled device, where the sensor's future data trends are predicted using a multi-class Support Vector Machine (SVM). A comparative study was conducted to investigate the effectiveness of our BBTP method on real-world datasets. Our results demonstrate that data trends predicted by BBTP achieve better precision, recall, and accuracy score when contrasted against four well-known techniques while reducing the space complexity by at least a factor of 10.

External IDs:dblp:conf/bigdataconf/AlemanPAK18