2023
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests Journal Article
In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 221-230, 2023, ISSN: 2832-7322.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@article{Bravenec2023c,
title = {UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/JISPIN.2023.3335882},
issn = {2832-7322},
year = {2023},
date = {2023-11-22},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {221-230},
abstract = {This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {article}
}
This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes: Dataset of Wi-Fi Probe Requests Proceedings Article
In: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@inproceedings{Bravenec2023b,
title = {UJI Probes: Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/IPIN57070.2023.10332508},
isbn = {979-8-3503-2012-1},
year = {2023},
date = {2023-09-25},
booktitle = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Approaching 6G Use Case Requirements with Multicasting Journal Article
In: IEEE Communications Magazine, vol. 61, no. 5, pp. 144-150, 2023, ISSN: 1558-1896.
Abstract | Links | BibTeX | Tags: 6G, A-wear, Internet of things, Wi-Fi
@article{Chukhno2023c,
title = {Approaching 6G Use Case Requirements with Multicasting},
author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/MCOM.001.2200659},
issn = {1558-1896},
year = {2023},
date = {2023-05-01},
journal = {IEEE Communications Magazine},
volume = {61},
number = {5},
pages = {144-150},
abstract = {The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.},
keywords = {6G, A-wear, Internet of things, Wi-Fi},
pubstate = {published},
tppubtype = {article}
}
The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.
2016
Mendoza-Silva, Germán Martín; Rodríguez-Pupo, Luis Enrique; Torres-Sospedra, Joaquín; Huerta-Guijarro, Joaquín
Solutions for signal mapping campaigns of Wi-Fi networks Proceedings
JIIDE 2016 Barcelona (27-30/09/2016), 2016.
Abstract | Links | BibTeX | Tags: Citizen Science, Data Infrastructures, Mobile apps, Web, Wi-Fi, Wi-Fi mapping
@proceedings{mendozasolutions,
title = {Solutions for signal mapping campaigns of Wi-Fi networks},
author = {Germán Martín Mendoza-Silva and Luis Enrique Rodríguez-Pupo and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro},
url = {http://www.idee.es/resources/presentaciones/JIIDE16/2016/34_art_2_UJI_SolucionesMapeadoWiFi.pdf},
year = {2016},
date = {2016-09-27},
publisher = {JIIDE 2016 Barcelona (27-30/09/2016)},
abstract = {The boom of smart mobile devices with several types of sensors has enabled applications that engage people in collecting information about their surroundings so that they can contribute to citizen science projects. In this paper, we
address a set of software solutions that aim to enable the general public to participate in WiFi signal samples collection campaigns. We expect these solutions to be appealing for researchers working in WiFi-based indoor positioning due to the widespread presence of WiFi antennas, the popularity of smartphone able to connect to those antennas, and because it is usually required to create a WiFi fingerprint database, which is a very time-consuming activity. The solutions set addresses three step in the WiFi signal samples database creation process: The campaign planning, the WiFi signal collection and the database construction and sharing. By the end of the process, the research community is provided with sets of geo-located points whose attributes include the signal intensities of the detected WiFi access points. The solutions set that we described in this paper can be extended to include campaigns focused on measuring other physical phenomena by using other sensors found in mobile devices.
},
keywords = {Citizen Science, Data Infrastructures, Mobile apps, Web, Wi-Fi, Wi-Fi mapping},
pubstate = {published},
tppubtype = {proceedings}
}
The boom of smart mobile devices with several types of sensors has enabled applications that engage people in collecting information about their surroundings so that they can contribute to citizen science projects. In this paper, we
address a set of software solutions that aim to enable the general public to participate in WiFi signal samples collection campaigns. We expect these solutions to be appealing for researchers working in WiFi-based indoor positioning due to the widespread presence of WiFi antennas, the popularity of smartphone able to connect to those antennas, and because it is usually required to create a WiFi fingerprint database, which is a very time-consuming activity. The solutions set addresses three step in the WiFi signal samples database creation process: The campaign planning, the WiFi signal collection and the database construction and sharing. By the end of the process, the research community is provided with sets of geo-located points whose attributes include the signal intensities of the detected WiFi access points. The solutions set that we described in this paper can be extended to include campaigns focused on measuring other physical phenomena by using other sensors found in mobile devices.
address a set of software solutions that aim to enable the general public to participate in WiFi signal samples collection campaigns. We expect these solutions to be appealing for researchers working in WiFi-based indoor positioning due to the widespread presence of WiFi antennas, the popularity of smartphone able to connect to those antennas, and because it is usually required to create a WiFi fingerprint database, which is a very time-consuming activity. The solutions set addresses three step in the WiFi signal samples database creation process: The campaign planning, the WiFi signal collection and the database construction and sharing. By the end of the process, the research community is provided with sets of geo-located points whose attributes include the signal intensities of the detected WiFi access points. The solutions set that we described in this paper can be extended to include campaigns focused on measuring other physical phenomena by using other sensors found in mobile devices.