Manifold Learning With Localized Procrustes Analysis Based WSN Localization

Behera, Adarsh Prasad; Singh, Abhishek; Verma, Shekhar; Kumar, Manish

doi:10.1109/lsens.2020.3025360

Cited by 17 publications

(11 citation statements)

References 10 publications

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“…Local trust zones [408]- [414] Sustainable develop. [415]- [417] Mapping [418], [419] Indoor [409], [410], [412], [413], [415]- [419] Outdoor [408], [414] Sensors [410], [412]- [419] Radar [409] mmWave/THz [408] AoD, AoA, RSS…”

Section: Rmse Rss Transfer Learningmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

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<p>When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learningbased localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.</p>

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Section: Rmse Rss Transfer Learningmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

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show abstract

“…In this experimental testing hall, the coordinates of the three points A, B and C are (0, 6), (0, 0), and (6, 0) respectively. In the RSSI data collection, we selected three points (3,2), (1,5), and (5, 5) to measure and collect the RSSI data. The number of RSSI data collected at each point is equal to 30.…”

Section: Measured Data Verficationmentioning

confidence: 99%

“…This is because in the real measurement environment, due to changes in the environment, the collected RSSI data have greater irregularities, so the positioning accuracy is lower than the simulated data. For the three selected points (3,2), (1,5), and (5,5), their localization accuracy have slight differences. The localization accuracy of point (3,2) is slightly better than (1,5), and (5, 5), which is caused by the anchor node deployment.…”

Section: Measured Data Verficationmentioning

confidence: 99%

“…For the three selected points (3,2), (1,5), and (5,5), their localization accuracy have slight differences. The localization accuracy of point (3,2) is slightly better than (1,5), and (5, 5), which is caused by the anchor node deployment. From the RMSE values given by point (5,5), we can see that WTM (n = 3) and WTM (n = N) give an identical value 0.95.…”

Section: Measured Data Verficationmentioning

confidence: 99%

“…Localization plays a very important role in wireless sensor networks (WSNs) [1][2][3]. Especially in some applications, where the operation of the network is heavily dependent on location information, positioning of sensor nodes becomes inevitable.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Yuan

Yue

et al. 2022

Electronics

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Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateration and averaged received signal strength indicator (RSSI). We propose an approach called weighted three minimum distances method (WTM) to deal with the poor accuracy of distances deduced from RSSI. Using a practical localization system, an experimental channel model is deduced to assess the performance of the proposed localization algorithm in realistic conditions. Both simulated data and measured data are used to verify the proposed method. Compared with nonlinear least squares (NLS), Levenberg–Marquardt algorithm (LM) and semidefinite programming method (SDP), simulations show that the proposed method exhibits better localization accuracy but consumes more calculation time.

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iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

et al. 2021

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Manifold Learning With Localized Procrustes Analysis Based WSN Localization

Cited by 17 publications

References 10 publications

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

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