Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Kaundal, Vivek; Sharma, Paawan; Prateek, Manish

doi:10.1515/eletel-2017-0054

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…In the proposed work, linear regression is used to estimate the distance value from the RSSI values and other channel parameters based on LNSM. LNSM parameters can be estimated from the RSSI values measured at both the outdoor and indoor locations [29,30]. The relationship between the measured RSSI and the logarithmic scale of the distance for indoor and outdoor locations are obtained using the regression curve fit.…”

Section: Distance Estimation Based On Regressionmentioning

confidence: 99%

Range‐based localisation of a wireless sensor network using Jaya algorithm

Meenalochani

Selvaraj

2019

IET Science, Measurement & Technology

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Section: Distance Estimation Based On Regressionmentioning

confidence: 99%

Range‐based localisation of a wireless sensor network using Jaya algorithm

Meenalochani

Selvaraj

2019

IET Science, Measurement & Technology

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“…The defined process is repeated after defined amount of time. After the threshold time, all the sensor nodes which hold the flag of localized will leave the flag and marked as non-localized [29][30]. The advance range based scheme for node localization required clock synchronization in the network.…”

Section: Advance Range Based Scheme For Node Localizationmentioning

confidence: 99%

Advancement in range based scheme for node localization of underwater acoustic networks

Khajuria

Kaur

2018

IJET

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The underwater acoustic network is the type of network which is deployed under the deep sea or ocean to gather underwater information. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node localization has a high delay which reduces its efficiency. The technique needs to be designed which localize a number of nodes in less amount of time. This research is based on the advancement of the range-based scheme for node localization. In the proposed scheme mobile beacons are responsible for the node localization. The beacon nodes send beacon message in the network and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node. The sensor nodes which are already localized will not respond back to the beacon messages which reduce delay in the network for node localization. The simulation of proposed modal is performed in MATLAB and it shows that proposed scheme performs well in terms of a number of nodes localized.

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“…In turn, for networks using ZigBee, a series of analyses of the impact of environmental conditions on the estimation of the model parameters were carried out [24,25]. In the context of the precision of distance estimation and location of ZigBee nodes, a comparative analysis for two models, the LNSM and the Hybrid Teacher Learning Based Optimisation Algorithm technique, respectively, was presented in [26]. A separate group of research works concerns the definition of generalised methods for determining the values of the parameters of path loss models and methods of correcting their values, which would allow the use of these methods in various environmental conditions and in WSN based on various technologies.…”

Section: Introductionmentioning

confidence: 99%

Application of Particle Filter in Path-loss Modelling

Wójcicki,

Zientarski,

Przyłucki

2023

Adv. Sci. Technol. Res. J.

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The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

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Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Cited by 6 publications

References 27 publications

Range‐based localisation of a wireless sensor network using Jaya algorithm

Range‐based localisation of a wireless sensor network using Jaya algorithm

Advancement in range based scheme for node localization of underwater acoustic networks

Application of Particle Filter in Path-loss Modelling

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