BMREWSN: Design of a hybrid Bio-inspired Model for improving the Routing performance of Energy-aware Wireless Sensor Networks

Kapoor, Ridhi; Sharma, Sandeep

doi:10.1109/cict56698.2022.9997975

Cited by 2 publications

(1 citation statement)

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“…For the training phase, the inputs and target outputs of the KELM are respectively the hop-counts number (between anchors and unknown nodes) and the anchors locations. Using the linear-kernel, the proposed method uses the real quantized hop-counts between unknown nodes as the test samples for the localization process in the exploitation phase [27]- [32].…”

Section: Introductionmentioning

confidence: 99%

Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

Liouane

Femmam

Bakir

et al. 2023

IJ-ICT

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Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.

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Section: Introductionmentioning

confidence: 99%