Pattern generation from event oriented sensor data using distributed sensor transaction model

Amount of data generated by IoT devices is increasing day by day, it requires efficient and scalable infrastructure to store and process this data. Distributed process is an idle tool for analyzing this amount of data. Pattern mining of the IoT device events can result in better decision making in IoT eco-system. In this paper we have proposed a integrated environment for distributed data collection using a Mapreduce based technique exploiting distributed processing of the data. The proposed approach uses an Apriori and Mapreduce based algorithm for analyzing the data. Available dataset and synthetic datasets has been used for analysis and scalability test of the proposed approach. The approach shows a good amount of scalabity for mining the frequent patterns in event based sensor data.

[1]  Iqbal Gondal,et al.  A novel algorithm for mining behavioral patterns from wireless sensor networks , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[2]  Peter Desnoyers,et al.  PRESTO: A Predictive Storage Architecture for Sensor Networks , 2005, HotOS.

[3]  Xueyan Lin,et al.  MR-Apriori: Association Rules algorithm based on MapReduce , 2014, 2014 IEEE 5th International Conference on Software Engineering and Service Science.

[4]  Diane J. Cook,et al.  Mining Sensor Streams for Discovering Human Activity Patterns over Time , 2010, 2010 IEEE International Conference on Data Mining.

[5]  Azzedine Boukerche,et al.  A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks , 2008, IEEE Transactions on Parallel and Distributed Systems.

[6]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[7]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[8]  Ming-Yen Lin,et al.  Apriori-based frequent itemset mining algorithms on MapReduce , 2012, ICUIMC.

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[11]  Yaser Jararweh,et al.  Cloudlet-based for big data collection in body area networks , 2013, 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013).

[12]  Qing He,et al.  Parallel Implementation of Apriori Algorithm Based on MapReduce , 2012, SNPD.

[13]  Ann Light,et al.  Designing Connected Products: UX for the Consumer Internet of Things , 2015 .

[14]  Lionel Touseau,et al.  Combining heterogeneous service technologies for building an Internet of Things middleware , 2012, Comput. Commun..

[15]  Zhen Liu,et al.  MapReduce as a programming model for association rules algorithm on Hadoop , 2010, The 3rd International Conference on Information Sciences and Interaction Sciences.

[16]  Peter Desnoyers,et al.  Ultra-low power data storage for sensor networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[17]  Duoduo Liao,et al.  On clusterization of "big data" streams , 2012, COM.Geo '12.

[18]  Jian Guo,et al.  Research on Improved A Priori Algorithm Based on Coding and MapReduce , 2013, 2013 10th Web Information System and Application Conference.

[19]  Antonio Gomariz,et al.  SPMF: a Java open-source pattern mining library , 2014, J. Mach. Learn. Res..

[20]  Luca Mainetti,et al.  Evolution of wireless sensor networks towards the Internet of Things: A survey , 2011, SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks.

[21]  Laurence T. Yang,et al.  Data Mining for Internet of Things: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[22]  Sanjay Ghemawat,et al.  MapReduce: simplified data processing on large clusters , 2008, CACM.

[23]  Xue-Zhou Chang.  Mapreduce-Apriori algorithm under cloud computing environment , 2015, 2015 International Conference on Machine Learning and Cybernetics (ICMLC).

[24]  Iqbal Gondal,et al.  Dependable large scale behavioral patterns mining from sensor data using Hadoop platform , 2017, Inf. Sci..