2023
Quezada-Gaibor, Darwin
Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint
@phdthesis{Quezada2023a,
title = {Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios},
author = {Darwin Quezada-Gaibor},
doi = {http://dx.doi.org/10.6035/14124.2023.821275},
year = {2023},
date = {2023-03-31},
school = {Universitat Jaume I. INIT},
abstract = {The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.},
keywords = {A-wear, Cloud computing, Indoor positioning, machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {phdthesis}
}
The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.
2022
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín
Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review Journal Article
In: Sensors, vol. 22, no. 1, pp. 110, 2022, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Cloud computing, Indoor positioning
@article{Quezada2022a,
title = {Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review},
author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},
doi = {https://doi.org/10.3390/s22010110},
issn = {1424-8220},
year = {2022},
date = {2022-01-15},
journal = {Sensors},
volume = {22},
number = {1},
pages = {110},
abstract = {Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.},
keywords = {Cloud computing, Indoor positioning},
pubstate = {published},
tppubtype = {article}
}
Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.
2016
Granell-Canut, Carlos; Havlik, Denis; Schade, Sven; Sabeur, Zoheir; Delaney, Conor; Pielorz, Jasmin; Usländer, Thomas; Mazzetti, Paolo; Schleidt, Katharina; Kobernus, Mike; Havlik, Fuada; Bodsberg, Nils Rune; Berre, Arne; Mon, Jose Lorenzo
Future Internet technologies for environmental applications Journal Article
In: Environmental Modelling & Software, vol. 78, pp. 1 - 15, 2016, ISSN: 1364-8152, (IF: 4.404 - 6/105 (Q1) Computer Science, Interdisciplinary Applications IF: 4.404 - 8/49 (Q1) Engineering, Environmental IF: 4.404 - 25/229 (Q1) Environmental Sciences ).
Abstract | Links | BibTeX | Tags: Big data, Cloud computing, Crowdtasking, Environmental informatics, Environmental observation web, Environmental specific enablers, Future internet, Internet of things, Volunteered geographic information
@article{Granell20161,
title = {Future Internet technologies for environmental applications},
author = {Carlos Granell-Canut and Denis Havlik and Sven Schade and Zoheir Sabeur and Conor Delaney and Jasmin Pielorz and Thomas Usländer and Paolo Mazzetti and Katharina Schleidt and Mike Kobernus and Fuada Havlik and Nils Rune Bodsberg and Arne Berre and Jose Lorenzo Mon},
url = {http://www.sciencedirect.com/science/article/pii/S1364815215301298},
doi = {http://dx.doi.org/10.1016/j.envsoft.2015.12.015},
issn = {1364-8152},
year = {2016},
date = {2016-04-08},
journal = {Environmental Modelling & Software},
volume = {78},
pages = {1 - 15},
abstract = {This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.},
note = {IF: 4.404 - 6/105 (Q1) Computer Science, Interdisciplinary Applications
IF: 4.404 - 8/49 (Q1) Engineering, Environmental
IF: 4.404 - 25/229 (Q1) Environmental Sciences
},
keywords = {Big data, Cloud computing, Crowdtasking, Environmental informatics, Environmental observation web, Environmental specific enablers, Future internet, Internet of things, Volunteered geographic information},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.