Predicting Complex Events for Pro-Active IoT Applications

Akbar, A, Moessner, K, Carrez, F and Zoha, A (2015) Predicting Complex Events for Pro-Active IoT Applications In: IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015-12-14 - 2015-12-16, Milan, Italy.

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Abstract

The widespread use of IoT devices has opened the possibilities for many innovative applications. Almost all of these applications involve analyzing complex data streams with low latency requirements. In this regard, pattern recognition methods based on CEP have the potential to provide solutions for analyzing and correlating these complex data streams in order to detect complex events. Most of these solutions are reactive in nature as CEP acts on real-time data and does not exploit historical data. In our work, we have explored a proactive approach by exploiting historical data using machine learning methods for prediction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a realworld use case. Our proposed architecture is generic and can be used across different fields for predicting complex events.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Authors :	Name Email ORCID Akbar, A Moessner, K Carrez, F Zoha, A
Date :	16 December 2015
Related URLs :	Publisher
Additional Information :	© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User :	Symplectic Elements
Date Deposited :	13 Jan 2016 12:33
Last Modified :	31 Oct 2017 17:59
URI:	http://epubs.surrey.ac.uk/id/eprint/809723

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