Compressive sensing and matrix completion in Wireless Sensor Networks

Kortas, Manel; Bouallègue, Ammar; Ezzeddine, Tahar; Meghdadi, Vahid; Habachi, Oussama; Cances, Jean-Pierre

doi:10.1109/iintec.2017.8325905

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…In [ 28 ], the authors have found that the data reconstruction performance of the MC depends on the compression ratio. In our previous work [ 29 ], we have illustrated that a simple MC-based approach requires a smaller fraction of sensor node readings. In [ 30 ], a state-of-the-art of MC-based algorithm for compressive data gathering has introduced the short-term stability with the low-rank feature.…”

Section: Related Workmentioning

confidence: 99%

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

Kortas

Habachi

Bouallègue

et al. 2021

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

Kortas

Habachi

Bouallègue

et al. 2021

Sensors

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“…Indeed, reducing the number of transmitting sensors, using methods such as CS, is not only useful to avoide the collisions but also crucial for sensors who need to sleep to prolong their lifetimes. Over the past years, a plenty of works has managed the data gathering problems in wireless networks by the integration of the CS technique, which made attractive progress in the network energy consumption [1]- [4]. Recently, it has been proven that the integration of Matrix Completion (MC), as an extension of CS, has significantly enhanced WSNs' performances.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, it has been proven that the integration of Matrix Completion (MC), as an extension of CS, has significantly enhanced WSNs' performances. Since MC treats the data in its matrix form, it can fully capture the signal correlation in both time and space, and hence achieves a good interpolation quality with a higher compression ratio (fewer delivered readings) [4]. Therefrom, many researches about data gathering schemes based on MC theory have been introduced [4]- [9].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach

Kortas

Habachi

Bouallègue

et al. 2019

2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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In this paper, we seek to address the data gathering in the continually growing Wireless Sensor Networks (WSNs) with the intention to save the nodes' energy. In order to address usual WSN problems, such as data losses, collisions and re-transmissions, a twofold data compression pattern is proposed. We consider that a restricted number of sensor nodes are selected to be active and represent the whole network, while the rest of nodes remain idle and do not participate at all in the data sensing and transmission. Furthermore, the set of active nodes' readings is efficiently reduced, in each time slot, according to the cluster scheduling. Relying on the existing Matrix Completion (MC) techniques, the sink node is unable to recover the entire data matrix due to the existence of completely empty rows that correspond to the inactive nodes, which can be considered as absent nodes for a very long period, or nodes that do not exist at all. Thereby, we propose a complementary interpolation technique, based on a minimization problem that benefits from sensor nodes inter-correlation, to guarantee the reconstruction of all the empty rows, despite their large number. The simulations confirm the efficiency of the proposed approach and show that it outperforms the existing one by up to 70.101% of Normalized Mean Absolute Error on all missed elements, when the number of active nodes is of about 10% of the total number of sensor nodes.

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The Energy-Aware Matrix Completion-Based Data Gathering Scheme for Wireless Sensor Networks

et al. 2020

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In the Wireless Sensor Networks (WSNs), ensuring long-term survival of the sensor devices is crucial, especially for non-energy harvesting networks where the sensors have to deal with the available limited power. Thus, there is a huge need to efficiently select, in each time-slot, a small set of source nodes to monitor the network area and deliver their data to the sink. Note that there is a trade-off between energy efficiency, achieved through data-compression, and the informative quality received by the sink. Moreover, although applying a high data compression ratio extremely reduces the overall network energy consumption, the network lifetime is not necessarily extended due to the uneven energy depletion of the nodes' batteries. To this end, in this paper, we propose the Energy-Aware Matrix Completion based data gathering approach (EAMC), which designates the active nodes according to their residual energy levels. To collect data readings, the proposed EAMC relies on a nodes clustering phase and a MC based data sampling. Then, the interpolation of all the missing data is performed by the sink thanks to a Three-stage MC based recovery framework. Since we are interested in high data loss scenarios, the limited amount of delivered data must be sufficient in terms of informative quality it holds in order to reach a satisfactory recovery accuracy for the entire data. Hence, the EAMC selects the nodes depending on their inter-correlation as well as the network energy efficiency, with the use of a combined energy-aware and correlation-based metric. This introduced active node cost function changes with the type of application one wants to perform with the intention to reach a longer lifespan for the network. Therewith, the numerical results show that the EAMC achieves an attractive and competitive trade-off between the data reconstruction quality and the network lifetime for all the investigated scenarios.

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Compressive sensing and matrix completion in Wireless Sensor Networks

Cited by 3 publications

References 25 publications

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

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

Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach

The Energy-Aware Matrix Completion-Based Data Gathering Scheme for Wireless Sensor Networks

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