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.
In this paper we proposed a novel Adaptive Distributed Wavelet Compression (ADWC) algorithm for reducing energy consumption in a wireless sensor network, where each of the sensors has limited power. This algorithm is characterized by a distributed lifting factorization, which matching well with the transmission strategy employed in wireless sensor networks., it also present an adaptive algorithm to selects the optimal wavelet compression parameters to minimize total energy dissipation. The simulation results showed that these approaches can achieve significant energy savings without sacrificing the quality of the data reconstruction.