Event Based Localization using Enhanced Multi Sequence Positioning in Wireless Sensor Networks

Iqbal, Mansoor; Kamal, Mohsin; Ansere, James Adu; Khan, Omar

doi:10.1109/inmic50486.2020.9318169

Cited by 1 publication

(1 citation statement)

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“…ese sensor nodes send information streams to ground stations either occasionally or in light of occasions, and the ground station sends the information to the objective node. One of the issues in WSN is the means by which to make an e cient organizational design among these nodes [2][3][4]. Localization plays a signi cant function.…”

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%