FSCEP: A New Model for Context Perception in Smart Homes

With the emergence of the Internet of Things and smart devices, smart homes are becoming more and more popular. The main goal of this study is to implement an event driven system in a smart home and to extract meaningful information from the raw data collected by the deployed sensors using Complex Event Processing (CEP). These high-level events can then be used by multiple smart home applications in particular situation identification. However, in real life scenarios, low-level events are generally uncertain. In fact, an event may be outdated, inaccurate, imprecise or in contradiction with another one. This can lead to misinterpretation from CEP and the associated applications. To overcome these weaknesses, in this paper, we propose a Fuzzy Semantic Complex Event Processing (FSCEP) model which can represent and reason with events by including domain knowledge and integrating fuzzy logic. It handles multiple dimensions of uncertainty, namely freshness, accuracy, precision and contradiction. FSCEP has been implemented and compared with a well known CEP. The results show how some ambiguities are solved.

[1]  Jai E. Jung,et al.  Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing , 2017, Future Gener. Comput. Syst..

[2]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

[3]  Stevan D. Vidich Complex Event Processing with Coral8 , 2008 .

[4]  Sebastian Rudolph,et al.  Stream reasoning and complex event processing in ETALIS , 2012, Semantic Web.

[5]  Manuel P. Cuéllar,et al.  A fuzzy ontology for semantic modelling and recognition of human behaviour , 2014, Knowl. Based Syst..

[6]  Johannes Gehrke,et al.  Cayuga: a high-performance event processing engine , 2007, SIGMOD '07.

[7]  Fusheng Wang,et al.  Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams , 2006, EDBT.

[8]  Opher Etzion,et al.  Event processing under uncertainty , 2012, DEBS.

[9]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[10]  Neil Immerman,et al.  On complexity and optimization of expensive queries in complex event processing , 2014, SIGMOD Conference.

[11]  Viktor K. Prasanna,et al.  Semantic Complex Event Processing over End-to-End Data Flows , 2012 .

[12]  Giordano Tamburrelli,et al.  Introducing uncertainty in complex event processing: model, implementation, and validation , 2014, Computing.

[13]  Francesco Orciuoli,et al.  A multi-agent fuzzy consensus model in a Situation Awareness framework , 2015, Appl. Soft Comput..