دانلود رایگان مقاله برنامه نویسی شبکه فشرده برای شبکه های حسگر بی سیم

BSTRACT

Considering the temporal and spatial correlations of sensor readings in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC), compressed sensing (CS) and spatio-temporal compression for correlated data. The proper selection of NC coefficients and measurement matrix is investigated for this scheme. This design ensures successful reconstruction of original data with a considerably high probability and enables successful deployment of NC and CS in a real field. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing independent encoding in each sensor node (including the cluster head nodes) and joint decoding in the sink node. In order to further reduce the reconstruction error, we construct a new optimization model of reconstruction error for the clustered spatio-temporal compression scheme. A distributed algorithm is developed to iteratively determine the optimal solution. Finally, simulation results verify that the clustered spatiotemporal compression scheme outperforms other two categories of compression schemes significantly in terms of recovery error and compression gain and the distributed algorithm converges to the optimal solution with a fast and stable speed.

7. Conclusion

Based on the temporal and spatial correlations of sensor readings, this paper proposed a clustered spatio-temporal compression scheme to reduce the number of transmissions and formulated a new optimization model to minimize the reconstruction error. The compression scheme could reduce the number of transmissions significantly. In the meantime, the design of NC encoding coef- ficients and measurement matrix was given for guaranteeing the reconstruction of clustered compression data successfully with an overwhelming probability. The proposed scheme also had lower reconstruction error and computational complexity by employing independent encoding in each sensor node and joint decoding in the sink node. In addition, in order to minimize the reconstruction error, a distributed algorithm was developed to achieve the optimal solution. Finally, the simulation results further confirmed the properties of the clustered spatio-temporal compression scheme and optimization model.