1 research outputs found
Decentralized Trajectory Tracking Using Homology and Hodge Decomposition in Sensor Networks
With the recent development of localization and tracking systems for both
indoor and outdoor settings, we consider the problem of sensing, representing
and analyzing human movement trajectories that we expect to gather in the near
future. In this paper, we propose to use the topological representation, which
records how a target moves around the natural obstacles in the underlying
environment. We demonstrate that the topological information can be
sufficiently descriptive for many applications and efficient enough for
storing, comparing and classifying these natural human trajectories. We
pre-process the sensor network with a purely decentralized algorithm such that
certain edges are given numerical weights. Then we can perform trajectory
classification by simply summing up the edge weights along the trajectory. Our
method supports real-time classification of trajectories with minimum
communication cost. We test the effectiveness of our approach by showing how to
classify randomly generated trajectories in a multi-level arts museum layout as
well as how to distinguish real world taxi trajectories in a large city.Comment: 30 pages, 10 figures, submitted to ACM TSA