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.
Data compression techniques have extensive applications in power-constrained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit D E, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit.