You are here

Real Time Analysis of Sensor Data for the Internet of Things by means of Clustering and Event Processing

Authors: 

Hugo Hromic, Danh Le Phuoc, Martin Serrano, Aleksander Antonic, Ivana P. Zarko, Conor Hayes, Stefan Decker

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics for the Internet of Things by using the OpenIoT1 middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real-time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real-time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as the interface for novelty analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms for specific interoperable IoT applications to enable analytics on data streams which can be regarded as the first step towards understanding complex phenomena, e.g., air pollution dynamics and its impact on human health.
Conference Name: 
The IEEE International Conference on Communications (ICC)
Digital Object Identifer (DOI): 
10.1109/ICC.2015.7248401
Publication Date: 
08/06/2015
Conference Location: 
United Kingdom (excluding Northern Ireland)
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes