Context-aware stream processing for distributed IoT applications

Most of the IoT applications are distributed in nature generating large data streams which have to be analyzed in near real-time. Solutions based on Complex Event Processing (CEP) have the potential to extract high-level knowledge from these data streams but the use of CEP for distributed IoT applications is still in early phase and involves many drawbacks. The manual setting of rules for CEP is one of the major drawback. These rules are based on threshold values and currently there are no automatic methods to find the optimized threshold values. In real-time dynamic IoT environments, the context of the application is always changing and the performance of current CEP solutions are not reliable for such scenarios. In this regard, we propose an automatic and context aware method based on clustering for finding optimized threshold values for CEP rules. We have developed a lightweight CEP called μCEP to run on low processing hardware which can update the rules on the run. We have demonstrated our approach using a real-world use case of Intelligent Transportation System (ITS) to detect congestion in near real-time.

[1]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Dan Puiu,et al.  Traffic condition monitoring using complex event processing , 2013, 2013 International Conference on System Science and Engineering (ICSSE).

[3]  Huadong Ma,et al.  Opportunities in mobile crowd sensing , 2014, IEEE Communications Magazine.

[4]  Younès Bennani,et al.  Unsupervised Learning for Analyzing the Dynamic Behavior of Online Banking Fraud , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[5]  D. Luckham.  The Power of Events , 2002 .

[6]  G. W. Milligan,et al.  A study of standardization of variables in cluster analysis , 1988 .

[7]  Slobodan Petrovic,et al.  A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters , 2006 .

[8]  Lucian Sasu,et al.  Autonomic monitoring approach based on CEP and ML for logistic of sensitive goods , 2014, IEEE 18th International Conference on Intelligent Engineering Systems INES 2014.

[9]  Friedemann Mattern,et al.  From the Internet of Computers to the Internet of Things , 2010, From Active Data Management to Event-Based Systems and More.

[10]  Ming-Whei Feng,et al.  Complex event processing for the Internet of Things and its applications , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[11]  Alejandro P. Buchmann,et al.  Complex Event Processing , 2009, it Inf. Technol..

[12]  Ilia Petrov,et al.  From Active Data Management to Event-Based Systems and More , 2010, Lecture Notes in Computer Science.

[13]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[14]  Yongheng Wang,et al.  A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things , 2014, Int. J. Distributed Sens. Networks.

[15]  Tamalika Chaira,et al.  A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images , 2011, Appl. Soft Comput..