SimpleTrack: Adaptive Trajectory Compression With Deterministic Projection Matrix for Mobile Sensor Networks

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using six data sets shows that our proposed algorithm can achieve submeter accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm shows that our algorithm can reduce the error by 10-60 cm for the same compression ratio.

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