Computational Intelligence for Localization of Mobile Wireless Sensor Networks

Kulkarni, Vaishali R; Desai, Veena

doi:10.1007/978-981-13-1135-2_34

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Towards increasing the efficiency of the model, the concept of resultant force vectors is applied, while the particle swarm optimization algorithm is employed to minimize the irregular deployment effects. The authors of [264] utilized the firefly algorithm and the artificial bee colony algorithm for approximating the distance of a mobile node from the anchor nodes. Also, a comparison between the two algorithms in terms of computation time and localization accuracy is presented.…”

Section: A Conventionalmentioning

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: A Conventionalmentioning

confidence: 99%

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

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Towards increasing the efficiency of the model, the concept of resultant force vectors is applied, while the particle swarm optimization algorithm is employed to minimize the irregular deployment effects. The authors of [296] utilized the firefly algorithm and the artificial bee colony algorithm for approximating the distance of a mobile node from the anchor nodes. Also, a comparison between the two algorithms in terms of computation time and localization accuracy is presented.…”

Section: ) Swarm Intelligencementioning

confidence: 99%

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

Trevlakis,

Boulogeorgos,

Pliatsios

et al. 2023

IEEE Open J. Commun. Soc.

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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 learning-based 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.

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Hybrid Approach for Trajectory Identification of Mobile Node via Lagrange Interpolation and Kalman Filtering Framework

Mishra¹,

Tripathi²,

Bajpai³

et al. 2020

Advances in Intelligent Systems and Computing

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Computational Intelligence for Localization of Mobile Wireless Sensor Networks

Cited by 6 publications

References 20 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

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

Hybrid Approach for Trajectory Identification of Mobile Node via Lagrange Interpolation and Kalman Filtering Framework

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