A novel clustering method for animal trajectory analysis using Wireless Sensor Network

Animal plays an important role in our Earth, researching the movements of animals is very helpful for us to conserve rare and precious species as well as food exploration. In this paper, we employ Wireless Sensor Networks (WSNs) with the potential for highly increased spatial and temporal resolution of measurement data. Hence WSNs promise enhanced tracking of animals without human intervention. To help experts making a better species and habitat assessment as well as conversation strategies, we propose an Extended Hierarchical Path clustering eHPCl method for analyzing the mobility of wild animals. A predictive mobility algorithm is also presented, which help experts solve the problems in data allocation and management. A system that simulates the mobility of animals is implemented. Performance of the proposed method is finally evaluated in terms of running time and estimation accuracy.