IoT data stream analytics

IoT Data Analytics Using Deep Learning

Xiaofeng Xie, Di Wu, Siping Liu, Renfa Li Abstract: Deep learning is a popular machine learning approach which has achieved a lot of progress in all traditional machine learning areas. Internet of thing (IoT) and Smart City deployments are generating large amounts of time-series sensor data in need of analysis. Applying deep learning to these domains has been an important topic of research. The...

Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications

Real-Time Big Data Stream Analytics

Big Data is a new term used to identify datasets that we cannot manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. MOA is a software fr...

Real‐time Iot Stream Processing and Large‐scale Data Analytics for Smart City Applications Semantic Data Stream Annotation for Automated Framework

Bringing IoT to Sports Analytics

This paper explores the possibility of bringing IoT to sports analytics, particularly to the game of Cricket. We develop solutions to track a ball’s 3D trajectory and spin with inexpensive sensors and radios embedded in the ball. Unique challenges arise rendering existing localization and motion tracking solutions inadequate. Our system, iBall, mitigates these problems by fusing disparate sourc...