Detection of Sensor Node-less Area Using A Genetic Algorithm for Wireless Sensor Network

Wibowo, Ferry Wahyu; Sediyono, Eko; Purnomo, Hindriyanto Dwi

doi:10.1109/isriti51436.2020.9315383

Cited by 3 publications

(1 citation statement)

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“…The aftereffects of group analysis state that components of groups are portrayed by the most extreme similarity, and there may be greater similarity among every WSN cluster. Subsequently, clustering methods are the productive ways of managing the previously mentioned issues and are broadly acknowledged in the field of data mining [ 11 ]. The neighborhood grid cluster (NGC) can understand data clustering naturally and adapt by means of self-assertive shape information deftly.…”

Section: Introductionmentioning

confidence: 99%

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Chen

Sackey

Ansere

et al. 2022

Computational Intelligence and Neuroscience

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Finding the location of sensors in wireless sensor networks (WSNs) is a major test, particularly in a wide region. A salient clustering approach is laid out to achieve better performance in such a network using an evolutional algorithm. This paper developed a clustered network called neighborhood grid cluster which has a node assuming the part of a cluster center focused in every grid. Grid-based clustering is less difficult and possesses a lot of benefits compared to other clustering techniques. Besides, we proposed a localization algorithm that centers around assessing the target area by considering the least estimated distance embedded with the genetic algorithm. Performance standards incorporate the energy representation, connectivity stratagem, and distance measure as fitness functions that assess our localization problem to demonstrate its viability. Simulation results confirm that our approach further improves localization accuracy, energy utilization, node lifetime, and localization coverage.

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Section: Introductionmentioning

confidence: 99%