Furfari, Francesco; Crivello, Antonino; Baronti, Paolo; Barsocchi, Paolo; Girolami, Michele; Palumbo, Filippo; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martín; Torres-Sospedra, Joaquín Discovering location based services: A unified approach for heterogeneous indoor localization systems Journal Article Internet of things, 13 , pp. 100334, 2020. Abstract | Links | BibTeX @article{Furfari2020,
title = {Discovering location based services: A unified approach for heterogeneous indoor localization systems},
author = {Francesco Furfari and Antonino Crivello and Paolo Baronti and Paolo Barsocchi and Michele Girolami and Filippo Palumbo and Darwin Quezada-Gaibor and Germán Martín Mendoza-Silva and Joaquín Torres-Sospedra },
doi = {https://doi.org/10.1016/j.iot.2020.100334 },
year = {2020},
date = {2020-02-04},
journal = {Internet of things},
volume = {13},
pages = {100334},
abstract = {The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture. |
Shubina, Victoriia; Holcer, Sylvia; Gould, Michael; Lohan, Elena Simona Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era Journal Article Data, 5 (4), pp. 87, 2020. Links | BibTeX @article{Shubina2020,
title = {Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era},
author = {Victoriia Shubina and Sylvia Holcer and Michael Gould and Elena Simona Lohan },
doi = {https://doi.org/10.3390/data5040087 },
year = {2020},
date = {2020-01-31},
journal = {Data},
volume = {5},
number = {4},
pages = {87},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Klus, Lucie; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari Crowdsourcing solutions for data gathering from wearables Conference 2019. Abstract | Links | BibTeX @conference{Klus2019,
title = {Crowdsourcing solutions for data gathering from wearables},
author = {Lucie Klus and Elena Simona Lohan and Carlos Granell-Canut and Jari Nurmi },
editor = {XXXV Finnish URSI Convention on Radio Science (URSI 2019), Tampere, Finland, 18 October 2019 (Session Wearable Computing)},
doi = {10.5281/zenodo.3528274 },
year = {2019},
date = {2019-10-08},
abstract = {This paper gives an overview of crowdsourcing databases and crowdsourcing-related challenges and open research issues for data collected from wearable devices. It is shown that,
with the advent of smarter wearable devices, the complexity of data gathering, storage, and processing in crowdsourced modes will increase exponentially and new solutions are needed in order to cope with larger data sets and low energy consumption in wearable devices, while ensuring the integrity and quality of the collected data.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper gives an overview of crowdsourcing databases and crowdsourcing-related challenges and open research issues for data collected from wearable devices. It is shown that,
with the advent of smarter wearable devices, the complexity of data gathering, storage, and processing in crowdsourced modes will increase exponentially and new solutions are needed in order to cope with larger data sets and low energy consumption in wearable devices, while ensuring the integrity and quality of the collected data. |
Casanova-Marqués, Raúl; Dzurenda, Petr; Hajny, Jan Implementation of Revocable Keyed-Verification Anonymous Credentials on Java Card Inproceedings Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-8, ACM, 0000, ISBN: 9781450396707. Abstract | Links | BibTeX @inproceedings{Casanova2022a,
title = {Implementation of Revocable Keyed-Verification Anonymous Credentials on Java Card},
author = {Raúl Casanova-Marqués and Petr Dzurenda and Jan Hajny},
doi = {https://doi.org/10.1145/3538969.3543798},
isbn = {9781450396707},
booktitle = {Proceedings of the 17th International Conference on Availability, Reliability and Security},
pages = {1-8},
publisher = {ACM},
abstract = {Java Card stands out as a good choice for the development of smart card applications due to the high interoperability between different manufacturers, its security, and wide support of cryptographic algorithms. Despite extensive cryptographic support, current Java Cards do not support non-standard cryptographic algorithms such as post-quantum, secure-multiparty computations, and privacy-enhancing cryptographic schemes. Moreover, Java Card is restricted by the Application Programming Interface (API) in algebraic operations, which are the foundation of modern cryptographic schemes. This paper addresses the issue of developing these modern schemes by exploiting the limited cryptographic API provided by these types of cards. We show how to (ab)use the Java Card’s API to perform modular arithmetic operations, as well as basic operations on elliptic curves. Furthermore, we implement an attribute-based privacy-enhancing scheme on an off-the-shelf Java Card. To do so, we use our cryptographic API and several optimization techniques to make the scheme as efficient as possible. To demonstrate the practicality of our solution, we present the implementation results and benchmark tests.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Java Card stands out as a good choice for the development of smart card applications due to the high interoperability between different manufacturers, its security, and wide support of cryptographic algorithms. Despite extensive cryptographic support, current Java Cards do not support non-standard cryptographic algorithms such as post-quantum, secure-multiparty computations, and privacy-enhancing cryptographic schemes. Moreover, Java Card is restricted by the Application Programming Interface (API) in algebraic operations, which are the foundation of modern cryptographic schemes. This paper addresses the issue of developing these modern schemes by exploiting the limited cryptographic API provided by these types of cards. We show how to (ab)use the Java Card’s API to perform modular arithmetic operations, as well as basic operations on elliptic curves. Furthermore, we implement an attribute-based privacy-enhancing scheme on an off-the-shelf Java Card. To do so, we use our cryptographic API and several optimization techniques to make the scheme as efficient as possible. To demonstrate the practicality of our solution, we present the implementation results and benchmark tests. |
Quezada-Gaibor, Darwin; Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Valkama, Mikko; Huerta-Guijarro, Joaquín; Torres-Sospedra, Joaquín Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive Journal Article IEEE Journal of Indoor and Seamless Positioning and Navigation, 1 , pp. 53-68, 0000, ISSN: 2832-7322. Abstract | Links | BibTeX @article{Quezada2023b,
title = {Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive},
author = {Darwin Quezada-Gaibor and Lucie Klus and Roman Klus and Elena Simona Lohan and Jari Nurmi and Mikko Valkama and Joaquín Huerta-Guijarro and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/JISPIN.2023.3299433},
issn = {2832-7322},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {53-68},
abstract = {Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline. |
