Efficient localization approach for large scale wireless sensor networks

Bouhdid, Badia; Akkari, Wafa; Belghith, Abdelfettah

doi:10.1109/iccp.2017.8117044

Cited by 1 publication

(2 citation statements)

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“…[18][19][20] Unlocalized nodes randomized their locations on beacon texts of anchor nodes requiring high strength. 21,22 Here, several methods to mitigate these connectivity costs have been proposed. If a node determines the wrong place, it spreads to the total network nodes those results in the dissemination [23][24][25][26][27][28] of incorrect information concerning the anchor nodes.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

Saravanan

2021

Int J Communication

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Summary The sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.

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Section: Introductionmentioning

confidence: 99%

“…These methods are effective but highly expensive with respect to energy consumption and refining 18–20 . Unlocalized nodes randomized their locations on beacon texts of anchor nodes requiring high strength 21,22 …”

Section: Introductionmentioning

confidence: 99%

A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

Saravanan

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

Efficient localization approach for large scale wireless sensor networks

Cited by 1 publication

References 13 publications

A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

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