A Two-Level Clustering based on Position, Data Correlation and Residual Energy in WSN

Fattoum, Marwa; Jellali, Zakia; Atallah, Leïla Najjar

doi:10.1109/iwcmc.2019.8766616

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…In [18], an energy efficient compressed data collection method based on compressive sensing theory and quantized compressed sensing is proposed. The authors in [19] propose a data aggregation method based on clustering network topology. The clustering approach is divided into two steps.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

2023

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Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management.

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Section: Related Workmentioning

confidence: 99%

“…To generate correlated readings of the sensors according to the spatial dimension, we adopt the most conventional correlation model, the exponential model [19] where the mutual spatial correlation between i th and j th nodes is given by:…”

Section: B Spatio-temporal Correlated Synthetic Data Generation 1) Sp...mentioning

confidence: 99%

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

2023

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“…In [14], an energy efficient compressed data collection method based on compressive sensing theory and quantized compressed sensing is proposed. The authors in [15] propose a data aggregation method based on clustering network topology. The clustering approach is divided into two steps.…”

Section: Related Workmentioning

confidence: 99%

“…Table 2 Simulation parameters. [15] where the mutual spatial correlation between i th and j th nodes is given by:…”

Section: Spatial Correlation Modelsmentioning

confidence: 99%

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic WSN

Fattoum¹,

Jellali²,

Najjar³

2021

Preprint

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Energy limitation is a major issue in WSN where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compare our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results show the effectiveness of our proposed method in optimizing energy consumption by reducing sampling rate while maintaining data quality.

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A Joint Clustering and Routing Algorithm based on GA for Multi Objective Optimization in WSN

Fattoum

Jellali

Atallah

2020

2020 IEEE Eighth International Conference on Communications and Networking (ComNet)

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A Two-Level Clustering based on Position, Data Correlation and Residual Energy in WSN

Cited by 4 publications

References 9 publications

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic WSN

A Joint Clustering and Routing Algorithm based on GA for Multi Objective Optimization in WSN

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