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Article

Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework

1
The XLIM Research Institute, University of Limoges, 87000 Limoges, France
2
SysCom Laboratory in the National Engineering School of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
*
Author to whom correspondence should be addressed.
This paper is an extension version of the conference paper: Kortas, M.; Habachi, O.; Bouallegue, A.; Meghdadi, V.; Ezzedine, T.; Cances, J.P. Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach. In Proceedings of the Software, Telecommunications and Computer Networks (SoftCOM), 2019 International Conference, Split, Croatia, 19–21 September 2019; pp. 1–6.
Academic Editor: Haris Pervaiz
Sensors 2021, 21(3), 1016; https://doi.org/10.3390/s21031016
Received: 16 December 2020 / Revised: 23 January 2021 / Accepted: 28 January 2021 / Published: 2 February 2021
(This article belongs to the Special Issue IoT for Smart Grids: Challenges, Opportunities and Trends)
In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme. View Full-Text
Keywords: Wireless Sensor Networks; Matrix Completion; data gathering; spatial data interpolation Wireless Sensor Networks; Matrix Completion; data gathering; spatial data interpolation
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MDPI and ACS Style

Kortas, M.; Habachi, O.; Bouallegue, A.; Meghdadi, V.; Ezzedine, T.; Cances, J.-P. Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework. Sensors 2021, 21, 1016. https://doi.org/10.3390/s21031016

AMA Style

Kortas M, Habachi O, Bouallegue A, Meghdadi V, Ezzedine T, Cances J-P. Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework. Sensors. 2021; 21(3):1016. https://doi.org/10.3390/s21031016

Chicago/Turabian Style

Kortas, Manel, Oussama Habachi, Ammar Bouallegue, Vahid Meghdadi, Tahar Ezzedine, and Jean-Pierre Cances. 2021. "Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework" Sensors 21, no. 3: 1016. https://doi.org/10.3390/s21031016

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