A Comprehensive Review On The Impact Of Compressed Sensing In Wireless Sensor Networks

Baskaran, K.; Blessing, R. Elijah; Lydia, M.

doi:10.21307/ijssis-2017-897

Cited by 7 publications

(3 citation statements)

References 93 publications

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“…Kumar et al (8) show the suitability of CS for such a recovery. Data recovery from signals is more precise with CS theory as the degree of sparsity increases.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning based efficient routing protocol in wireless sensor network

Madkar

Pardeshi²,

Kumbhar

2022

Salud, Ciencia y Tecnología

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Data loss and recovery are important factors that directly affect the efficiency of the wireless sensor network (WSN). The wireless channel characteristics have a significant impact on data transmission and reception. On the receiver side, the most difficult tasks are maximizing packet delivery ratio and recovering lost data. In some cases, cyclic redundancy check (CRC) based algorithms can provide better data recovery. The CRC method can be made adaptive by using channel characteristics to correct the error bits. This paper evaluates the performance of the proposed machine learning-based efficient routing protocol (ML-ERP). For data recovery, the CRC with channel impulse response (CIR) prediction based on sensor node location information was used. The data recovery capability of ML-ERP increased the network efficiency in terms of packet delivery ratio. Also, due to less data loss, the energy efficiency of the network was also improved by almost 6 % ove existing protocols.

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“…Kumar et al (8) show the suitability of CS for such a recovery. Data recovery from signals is more precise with CS theory as the degree of sparsity increases.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning based efficient routing protocol in wireless sensor network

Madkar

Pardeshi²,

Kumbhar

2022

Salud, Ciencia y Tecnología

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“…One popular postulate of such low-dimensional structures is sparsity, which is, a signal can be simply represented with a few non-zero coefficients in an invertible proper sparsifying domain [7]. CS has been introduced as a good fit for such application in both the acquisition and reconstruction of the signal [8]. With a number of measurements proportional to the sparsity level, CS enables the reliable reconstruction of the signal.…”

Section: Related Workmentioning

confidence: 99%

“…In (8), if pct Nrep % × cl j is not an integer, we round N rep j to the nearest integer greater than or equal to the value of that element. Here, the selection of the sets N rep j of clusters' representative nodes is independent from one cluster to another.…”

Section: Sampling Patternmentioning

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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