Data Reconstruction of Wireless Sensor Network Based on Graph Signal


  • Zhiyang Xu



Data reconstruction, graph signal processing, smoothness, Taylor series.


The environmental and other factors can cause data missing in power systems; thus, data reconstruction is of great significance. In this paper, we model the observed signal as time-varying signal based on graph signal processing (GSP) and establish an optimization problem with the objective of minimizing the error between the true signal and the reconstructed signal at the sampling points and improving the smoothness of the reconstructed signal. To solve the optimization problem, Taylor series expansion is performed on the Hessian inverse matrix of the objective function, and truncated Taylor series is used as an approximation of the Hessian inverse matrix. In the simulation, the algorithm proposed in this paper is compared with the gradient descent algorithm, and the result shows that the proposed algorithm converges faster and the reconstructed signal is more accurate.


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Wang X, Liu P, Gu Y. Local-set-based graph signal reconstruction[J]. IEEE transactions on signal processing, 2015, 63(9): 2432-2444.

Jablonski I. Graph signal processing in applications to sensor networks, smart grids, and smart cities[J]. IEEE Sensors Journal, 2017, 17(23): 7659-7666.

Berger P, Hannak G, Matz G. Graph signal recovery via primal-dual algorithms for total variation minimization[J]. IEEE Journal of Selected Topics in Signal Processing, 2017, 11(6): 842-855.

Lorenzo P D, Barbarossa S, Banelli P. Sampling and recovery of graph signals[M]//Cooperative and Graph Signal Processing. Academic Press, 2018: 261-282.

Mao X, Gu Y. A Joint Detection and Reconstruction Method for Blind Graph Signal Recovery[C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018: 4184-4188.

Hasnat M A, Rahnamay-Naeini M. Power System State Recovery using Local and Global Smoothness of its Graph Signals[C]//2022 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2022: 01-05.

Sandryhaila A, Moura J M F. Discrete signal processing on graphs: Frequency analysis[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3042-3054.

Narang S K, Gadde A, Sanou E, et al. Localized iterative methods for interpolation in graph structured data[C]//2013 IEEE Global Conference on Signal and Information Processing. IEEE, 2013: 491-494.




How to Cite

“Data Reconstruction of Wireless Sensor Network Based on Graph Signal” (2023) Transactions on Engineering and Technology Research, 1, pp. 129–136. doi:10.62051/zxjqh323.