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

TL;DR

SimpleTrack introduces an adaptive lossy trajectory compression method for mobile sensor networks using a deterministic projection matrix and speed-based prediction, achieving high accuracy and outperforming existing methods.

Contribution

The paper presents a novel adaptive compression algorithm with a deterministic projection matrix and speed-based projection prediction, improving accuracy and efficiency.

Findings

Achieves sub-metre accuracy in trajectory reconstruction.

Outperforms existing projection matrix methods.

Reduces error by 10-60 cm compared to state-of-the-art algorithms.

Abstract

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 6 datasets shows that our proposed algorithm can achieve sub-metre 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 show that our algorithm can reduce the error by 10-60 cm for the same compression ratio.