Identify individuals behaviors based GPS trajectories in the Internet of Things
Fang Zhou, Chao Ma, Xizhong Wang, Jiaxing Qu, Shuli Zhang · 2016
Nowadays, with rapid development of technology, Internet, mobile Internet, Internet of things and sensor network, cyberspace has expanded to a ubiquitous space of human beings, machines and Internet of things. The location of people is one of the most important feature in the Internet of things (IOTs). Therefore, we focus on identifying individuals behaviors based on their GPS trajectories to support IoTs applications. Firstly, we propose a transform method that transforms a GPS trajectory into a sequence of POI (points of interest) based on the spatial and temporal property of GPS points to compress information effectively. Then, we implement a novel periodic-frequent POI sets mining method to discover the POI sets which are not only occurring frequently, but also appearing periodically. Finally, experimental results show the efficiency and stability of the algorithm.