A Framework for Improving Movement Detection by Implementing Data Mining on Big Data of Internet of Things

Era of internet of things (IOT) and big data of it has begun, in this era data mining can playvery important role by transforming big data to useful information. In this paper author reviewssome data mining techniques that can be implemented on big data generated by sensors of IOTto detect human movement types. Additionally, one role player sensor in IOT calledaccelerometer and different types of data it generates was reviewed. Moreover, differenttechniques that was used by other researchers to detect human movement types using datamining on accelerometer data was critically reviewed. Based on these works and otherliterature reviews, author proposes a framework to discover human movements types withimproved usage of data mining on IOT. The proposed framework implements four type of datamining methods in addition to a ranking system to produce the results. Using proposedframework, type of human movements can be identified with respect to the time of the day inaddition to accelerometer data.

Keywords:

Big data , Internet of things , Data mining , Movement detection , Framework