2024
Casanova-Marqués, Raúl
Privacy-enhancing technologies and privacy-enhancing cryptography for wearables PhD Thesis
Brno University of Technology, 2024.
Abstract | Links | BibTeX | Tags: A-wear, cryptography, Indoor positioning, Internet of things, privacy protection, wearables
@phdthesis{Casanova2024a,
title = {Privacy-enhancing technologies and privacy-enhancing cryptography for wearables},
author = {Raúl Casanova-Marqués},
url = {http://hdl.handle.net/10803/690875},
doi = {http://dx.doi.org/10.6035/14124.2024.839804},
year = {2024},
date = {2024-04-29},
urldate = {2024-04-29},
school = {Brno University of Technology},
abstract = {In response to escalating privacy concerns and the need for secure digital communication, cryptographic mechanisms have been developed to ensure impervious information exchange. However, traditional cryptographic approaches are inadequate in dynamic and resource-constrained environments, such as wearable devices. This thesis investigates attribute-based credential schemes, offering fine-grained access control based on user-specific attributes. Specifically, it assesses the effectiveness and scalability of attribute-based anonymous credential schemes within dynamic wearable device architectures. The study focuses on enhancing these schemes by incorporating user revocation while preserving privacy. Additionally, the research develops methods for attribute-based authentication protocols on smart cards with limited elliptic curve cryptography support and addresses usability challenges. Furthermore, the thesis explores the integration of anonymous authentication in collaborative indoor positioning systems to ensure privacy and security. It also delves into implementing attribute-based authentication in resource-constrained environments, including Internet of Things devices, and evaluating their feasibility in dynamic wearable device architectures.},
keywords = {A-wear, cryptography, Indoor positioning, Internet of things, privacy protection, wearables},
pubstate = {published},
tppubtype = {phdthesis}
}
In response to escalating privacy concerns and the need for secure digital communication, cryptographic mechanisms have been developed to ensure impervious information exchange. However, traditional cryptographic approaches are inadequate in dynamic and resource-constrained environments, such as wearable devices. This thesis investigates attribute-based credential schemes, offering fine-grained access control based on user-specific attributes. Specifically, it assesses the effectiveness and scalability of attribute-based anonymous credential schemes within dynamic wearable device architectures. The study focuses on enhancing these schemes by incorporating user revocation while preserving privacy. Additionally, the research develops methods for attribute-based authentication protocols on smart cards with limited elliptic curve cryptography support and addresses usability challenges. Furthermore, the thesis explores the integration of anonymous authentication in collaborative indoor positioning systems to ensure privacy and security. It also delves into implementing attribute-based authentication in resource-constrained environments, including Internet of Things devices, and evaluating their feasibility in dynamic wearable device architectures.
Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari
EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets Journal Article
In: IEEE Transactions on Mobile Computing, vol. 25, no. 5, pp. 3589-3604, 2024, ISSN: 1558-0660.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, prediction algorithms, Wi-Fi fingerprint
@article{Klus2024a,
title = {EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets},
author = {Lucie Klus and Roman Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Carlos Granell-Canut and Jari Nurmi},
doi = {10.1109/TMC.2023.3277333},
issn = {1558-0660},
year = {2024},
date = {2024-03-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {25},
number = {5},
pages = {3589-3604},
abstract = {Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.},
keywords = {A-wear, machine learning, prediction algorithms, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.
2023
Bravenec, Tomás
Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning
@phdthesis{Bravenec2023d,
title = {Exploiting Wireless Communications for Localization: Beyond Fingerprinting},
author = {Tomás Bravenec},
url = {http://hdl.handle.net/10803/689593},
doi = {http://dx.doi.org/10.6035/14124.2023.868082},
year = {2023},
date = {2023-12-18},
school = {Universitat Jaume I. INIT},
abstract = {The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.},
keywords = {A-wear, data analysis methods, geoprivacy, Indoor positioning, machine learning},
pubstate = {published},
tppubtype = {phdthesis}
}
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
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.
Bravenec, Tomás; Gould, Michael; Fryza, Tomas; Torres-Sospedra, Joaquín
Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article
In: IEEE Sensors Journal, vol. 17, pp. 20044-20054, 2023, ISSN: 1530-437X.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, radio maps
@article{Bravenec2023e,
title = {Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms},
author = {Tomás Bravenec and Michael Gould and Tomas Fryza and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/JSEN.2023.3296752},
issn = {1530-437X},
year = {2023},
date = {2023-08-01},
journal = {IEEE Sensors Journal},
volume = {17},
pages = {20044-20054},
abstract = {Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.},
keywords = {A-wear, Indoor positioning, radio maps},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.
Casanova-Marqués, Raúl; Torres-Sospedra, Joaquín; Hajny, Jan; Gould, Michael
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article
In: Internet of Things, vol. 22, pp. 100801, 2023, ISSN: 2542-6605.
Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, wearables
@article{Casanova2023a,
title = {Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs},
author = {Raúl Casanova-Marqués and Joaquín Torres-Sospedra and Jan Hajny and Michael Gould},
doi = {https://doi.org/10.1016/j.iot.2023.100801},
issn = {2542-6605},
year = {2023},
date = {2023-07-01},
journal = {Internet of Things},
volume = {22},
pages = {100801},
abstract = {The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.},
keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, wearables},
pubstate = {published},
tppubtype = {article}
}
The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.
Pascacio-de-los-Santos, Pavel
Collaborative Techniques for Indoor Positioning Systems PhD Thesis
Universitat Jaume I. INIT, 2023, ISBN: 978-952-03-2905-1.
Abstract | Links | BibTeX | Tags: A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint
@phdthesis{Pascacio2023a,
title = {Collaborative Techniques for Indoor Positioning Systems},
author = {Pavel Pascacio-de-los-Santos},
url = {http://hdl.handle.net/10803/688489},
doi = {http://dx.doi.org/10.6035/14124.2023.821144},
isbn = {978-952-03-2905-1},
year = {2023},
date = {2023-06-09},
school = {Universitat Jaume I. INIT},
abstract = {This doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE–RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits.},
keywords = {A-wear, Bluetooth Low Energy, Indoor positioning, machine learning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {phdthesis}
}
This doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE–RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits.
Chukhno, Nadezhda; Chukhno, Olga; Moltchanov, Dmitri; Molinaro, Antonella; Gaidamaka, Yuliya; Samouylov, Konstantin; Koucheryavy, Yevgeni; Araniti, Giuseppe
Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas Journal Article
In: IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3572 - 3588, 2023, ISSN: 1558-0660.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables
@article{Chukhno2023a,
title = {Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas},
author = {Nadezhda Chukhno and Olga Chukhno and Dmitri Moltchanov and Antonella Molinaro and Yuliya Gaidamaka and Konstantin Samouylov and Yevgeni Koucheryavy and Giuseppe Araniti},
doi = {10.1109/TMC.2021.3136298},
issn = {1558-0660},
year = {2023},
date = {2023-06-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {22},
number = {6},
pages = {3572 - 3588},
abstract = {The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.},
keywords = {A-wear, machine learning, wearables},
pubstate = {published},
tppubtype = {article}
}
The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.
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.
Klus, Lucie
From Compression of Wearable-based Data to Effortless Indoor Positioning PhD Thesis
Tampere University. Faculty of Information Technology and Communication Sciences, 2023, ISBN: 978-952-03-2832-0.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, machine learning, wearables
@phdthesis{Klus2023a,
title = {From Compression of Wearable-based Data to Effortless Indoor Positioning},
author = {Lucie Klus},
url = {http://hdl.handle.net/10803/688947},
doi = {http://dx.doi.org/10.6035/14124.2023.45900046},
isbn = {978-952-03-2832-0},
year = {2023},
date = {2023-04-27},
school = {Tampere University. Faculty of Information Technology and Communication Sciences},
abstract = {In recent years, wearable devices have become ever-present in modern society. They
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.},
keywords = {A-wear, Indoor positioning, machine learning, wearables},
pubstate = {published},
tppubtype = {phdthesis}
}
In recent years, wearable devices have become ever-present in modern society. They
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.
are typically defined as small, battery-restricted devices, worn on, in, or in very close
proximity to a human body. Their performance is defined by their functionalities as
much as by their comfortability and convenience. As such, they need to be compact
yet powerful, thus making energy efficiency an extremely important and relevant
aspect of the system. The market of wearable devices is nowadays dominated by
smartwatches and fitness bands, which are capable of gathering numerous sensorbased
data such as temperature, pressure, heart rate, or blood oxygen level, which
have to be processed in real-time, stored, or wirelessly transferred while consuming
as little energy as possible to ensure long battery life. Implementing compression
schemes directly at the wearable device is one of the relevant methods to reduce the
volume of data and to minimize the number of required operations while processing
them, as raw measurements include plenty of redundancies that can be removed
without damaging the useful information itself.
Fryza, Tomas; Bravenec, Tomás; Kohl, Zdenek
Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings Proceedings Article
In: 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA, pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-9835-9.
Abstract | Links | BibTeX | Tags: A-wear, Indoor localization, Smart Cities
@inproceedings{Bravenec2023a,
title = {Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings},
author = {Tomas Fryza and Tomás Bravenec and Zdenek Kohl},
doi = {10.1109/RADIOELEKTRONIKA57919.2023.10109085},
isbn = {979-8-3503-9835-9},
year = {2023},
date = {2023-04-19},
booktitle = {2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA},
pages = {1-6},
publisher = {IEEE},
abstract = {We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.},
keywords = {A-wear, Indoor localization, Smart Cities},
pubstate = {published},
tppubtype = {inproceedings}
}
We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.
Chukhno, Nadezhda
Direct Communication radio interface for new radio multicasting and cooperative positioning PhD Thesis
Università Reggio Calabria, 2023.
Abstract | Links | BibTeX | Tags: 5G, A-wear, Indoor positioning
@phdthesis{Chukhno2023d,
title = {Direct Communication radio interface for new radio multicasting and cooperative positioning},
author = {Nadezhda Chukhno},
url = {https://hdl.handle.net/20.500.12318/136586},
year = {2023},
date = {2023-04-03},
address = {Reggio Calabria},
school = {Università Reggio Calabria},
abstract = {Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology.},
keywords = {5G, A-wear, Indoor positioning},
pubstate = {published},
tppubtype = {phdthesis}
}
Recently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its vantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sidelink aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology.
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.
Chukhno, Nadezhda; Chukhno, Olga; Moltchanov, Dmitri; Gaydamaka, Anna; Samuylov, Andrey; Molinaro, Antonella; Koucheryavy, Yevgeni; Iera, Antonio
The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems Journal Article
In: IEEE Transactions on Broadcasting, vol. 69, no. 1, pp. 201-214, 2023, ISSN: 1557-9611.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables
@article{Chukhno2023b,
title = {The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems},
author = {Nadezhda Chukhno and Olga Chukhno and Dmitri Moltchanov and Anna Gaydamaka and Andrey Samuylov and Antonella Molinaro and Yevgeni Koucheryavy and Antonio Iera},
doi = {10.1109/TBC.2022.3206595},
issn = {1557-9611},
year = {2023},
date = {2023-03-01},
journal = {IEEE Transactions on Broadcasting},
volume = {69},
number = {1},
pages = {201-214},
abstract = {Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.},
keywords = {A-wear, machine learning, wearables},
pubstate = {published},
tppubtype = {article}
}
Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.
2022
Chukhno, Olga; Chukhno, Nadezhda; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services Proceedings Article
In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 5850-5855, IEEE, 2022, ISBN: 978-1-6654-3541-3.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@inproceedings{Chukhno2022d,
title = {Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services},
author = {Olga Chukhno and Nadezhda Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/GLOBECOM48099.2022.10000930},
isbn = {978-1-6654-3541-3},
year = {2022},
date = {2022-12-08},
booktitle = {GLOBECOM 2022 - 2022 IEEE Global Communications Conference},
pages = {5850-5855},
publisher = {IEEE},
abstract = {According to the 6G vision, the evolution of wireless communication systems will soon lead to the possibility of supporting Tbps communications, as well as satisfying, individually or jointly, a plethora of other very stringent quality requirements related to latency, bitrate, and reliability. The achievement of these goals will naturally raise many research issues within radio communications. In this context, a promising 6G wireless communications enabler is the reconfigurable intelligent surface (RIS) hardware architecture, which has already been recognized as a game-changing way to turn any naturally passive wireless communication setting into an active one. This paper investigates RIS-aided multicast 6G communications by first modeling the system delay as a first-come-first-served (FCFS) M/D/1 queue and analyzing the behavior under different blockage conditions. Then the study of multi-beam operation scenarios, covering multicast and RIS-aided multicast communications, is conducted by leveraging an M/D/c queue model. Achieved results show that large-size RISs outperform even slightly obstructed direct BS-to-user paths. In contrast, RISs of smaller sizes require the design of sophisticated power control and sharing mechanisms to achieve better performance.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
According to the 6G vision, the evolution of wireless communication systems will soon lead to the possibility of supporting Tbps communications, as well as satisfying, individually or jointly, a plethora of other very stringent quality requirements related to latency, bitrate, and reliability. The achievement of these goals will naturally raise many research issues within radio communications. In this context, a promising 6G wireless communications enabler is the reconfigurable intelligent surface (RIS) hardware architecture, which has already been recognized as a game-changing way to turn any naturally passive wireless communication setting into an active one. This paper investigates RIS-aided multicast 6G communications by first modeling the system delay as a first-come-first-served (FCFS) M/D/1 queue and analyzing the behavior under different blockage conditions. Then the study of multi-beam operation scenarios, covering multicast and RIS-aided multicast communications, is conducted by leveraging an M/D/c queue model. Achieved results show that large-size RISs outperform even slightly obstructed direct BS-to-user paths. In contrast, RISs of smaller sizes require the design of sophisticated power control and sharing mechanisms to achieve better performance.
Chukhno, Olga; Chukhno, Nadezhda; Araniti, Giuseppe; Campolo, Claudia; Iera, Antonio; Molinaro, Antonella
Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks Journal Article
In: IEEE Internet of Things Journal , vol. 9, no. 23, pp. 23927 - 23940, 2022, ISSN: 2327-4662.
Abstract | Links | BibTeX | Tags: A-wear, digital twin, Internet of things
@article{Chukhno2022c,
title = {Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks},
author = {Olga Chukhno and Nadezhda Chukhno and Giuseppe Araniti and Claudia Campolo and Antonio Iera and Antonella Molinaro},
doi = {0.1109/JIOT.2022.3190737},
issn = {2327-4662},
year = {2022},
date = {2022-12-01},
journal = {IEEE Internet of Things Journal },
volume = {9},
number = {23},
pages = {23927 - 23940},
abstract = {As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.},
keywords = {A-wear, digital twin, Internet of things},
pubstate = {published},
tppubtype = {article}
}
As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.
Chukhno, Olga; Chukhno, Nadezhda; Galinina, Olga; Andreev, Sergey; Gaidamaka, Yuliya; Samouylov, Konstantin; Araniti, Giuseppe
A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks Journal Article
In: IEEE Transactions on Wireless Communications , vol. 21, no. 9, pp. 7491 - 7505, 2022, ISSN: 1558-2248.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@article{Chukhno2022b,
title = {A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks},
author = {Olga Chukhno and Nadezhda Chukhno and Olga Galinina and Sergey Andreev and Yuliya Gaidamaka and Konstantin Samouylov and Giuseppe Araniti},
doi = {10.1109/TWC.2022.3159086},
issn = {1558-2248},
year = {2022},
date = {2022-09-22},
journal = {IEEE Transactions on Wireless Communications },
volume = {21},
number = {9},
pages = {7491 - 7505},
abstract = {The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {article}
}
The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.
Brancati, Gianluca; Chukhno, Olga; Chukhno, Nadezhda; Araniti, Giuseppe
Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis Proceedings Article
In: 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications , pp. 1-6, IEEE, 2022, ISBN: 978-1-6654-8054-3.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@inproceedings{Brancati2022a,
title = {Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis},
author = {Gianluca Brancati and Olga Chukhno and Nadezhda Chukhno and Giuseppe Araniti},
doi = {10.1109/PIMRC54779.2022.9978019},
isbn = {978-1-6654-8054-3},
year = {2022},
date = {2022-09-22},
booktitle = {2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications },
pages = {1-6},
publisher = {IEEE},
abstract = {The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m.
Ricci, Sara; Dzurenda, Petr; Casanova-Marqués, Raúl; Cika, Petr
Threshold Signature for Privacy-Preserving Blockchain Proceedings Article
In: Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022, Springer, Cham, 2022, ISBN: 978-3-031-16167-4.
Abstract | Links | BibTeX | Tags: A-wear, blockchain
@inproceedings{Ricci2022a,
title = {Threshold Signature for Privacy-Preserving Blockchain},
author = {Sara Ricci and Petr Dzurenda and Raúl Casanova-Marqués and Petr Cika},
doi = {https://doi.org/10.1007/978-3-031-16168-1_7},
isbn = {978-3-031-16167-4},
year = {2022},
date = {2022-09-07},
booktitle = { Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022},
volume = {459},
publisher = {Springer, Cham},
series = { Lecture Notes in Business Information Processing},
abstract = {Threshold signatures received renewed interest in recent years due to their practical applicability to Blockchain technology. In this article, we propose a novel (n, t)-threshold signature scheme suitable for increasing security and privacy in Blockchain technology. Our scheme allows splitting a Blockchain wallet into multiple devices so that a threshold of them is needed for signing. This increases the security of the transactions, e.g., more devices need to be compromised to recover the key and permits, and the privacy, e.g., the signing is made anonymously on behalf of the group of users sharing the Blockchain wallet. Our experimental results show that the signing algorithm requires less than 10 ms in the cases of 10 devices involved.},
keywords = {A-wear, blockchain},
pubstate = {published},
tppubtype = {inproceedings}
}
Threshold signatures received renewed interest in recent years due to their practical applicability to Blockchain technology. In this article, we propose a novel (n, t)-threshold signature scheme suitable for increasing security and privacy in Blockchain technology. Our scheme allows splitting a Blockchain wallet into multiple devices so that a threshold of them is needed for signing. This increases the security of the transactions, e.g., more devices need to be compromised to recover the key and permits, and the privacy, e.g., the signing is made anonymously on behalf of the group of users sharing the Blockchain wallet. Our experimental results show that the signing algorithm requires less than 10 ms in the cases of 10 devices involved.
Dzurenda, Petr; Casanova-Marqués, Raúl; Malina, Lukas; Hajny, Jan
Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials Proceedings Article
In: Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-9, ACM, 2022, ISBN: 9781450396707.
Abstract | Links | BibTeX | Tags: A-wear, geoprivacy, wearables
@inproceedings{Dzurenda2022a,
title = {Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials},
author = {Petr Dzurenda and Raúl Casanova-Marqués and Lukas Malina and Jan Hajny},
doi = {https://doi.org/10.1145/3538969.3543803},
isbn = {9781450396707},
year = {2022},
date = {2022-08-01},
booktitle = {Proceedings of the 17th International Conference on Availability, Reliability and Security},
pages = {1-9},
publisher = {ACM},
abstract = {With the daily increase in digitalization and integration of the physical and digital worlds, we need to better protect users’ privacy and identity. Attribute-based Credentials (ABCs) seem to be a promising technology for this task. In this paper, we provide comprehensive analyses of the readiness, maturity, and applicability of ABCs to real-world applications. Furthermore, we introduce our Privacy-Enhancing Authentication System (PEAS), which is based on ABCs and meets all privacy requirements such as anonymity and unlinkability of the user’s activities. Besides privacy features, PEAS also provides revocation mechanisms to identify and revoke malicious users. The system is suitable for deployment in real-world scenarios and runs on a wide range of user devices (e.g., smart cards, smartphones, and wearables).},
keywords = {A-wear, geoprivacy, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
With the daily increase in digitalization and integration of the physical and digital worlds, we need to better protect users’ privacy and identity. Attribute-based Credentials (ABCs) seem to be a promising technology for this task. In this paper, we provide comprehensive analyses of the readiness, maturity, and applicability of ABCs to real-world applications. Furthermore, we introduce our Privacy-Enhancing Authentication System (PEAS), which is based on ABCs and meets all privacy requirements such as anonymity and unlinkability of the user’s activities. Besides privacy features, PEAS also provides revocation mechanisms to identify and revoke malicious users. The system is suitable for deployment in real-world scenarios and runs on a wide range of user devices (e.g., smart cards, smartphones, and wearables).
Casanova-Marqués, Raúl; Dzurenda, Petr; Hajny, Jan
Implementation of Revocable Keyed-Verification Anonymous Credentials on Java Card Proceedings Article
In: Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-8, ACM, 2022, ISBN: 9781450396707.
Abstract | Links | BibTeX | Tags: A-wear, geoprivacy
@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},
year = {2022},
date = {2022-06-01},
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 = {A-wear, geoprivacy},
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.
2021
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Efficient Management of Multicast Traffic in Directional mmWave Networks Journal Article
In: IEEE Transactions on Broadcasting, vol. 67, no. 3, pp. 593-605, 2021, ISSN: 1557-9611.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@article{Chukhno2021a,
title = {Efficient Management of Multicast Traffic in Directional mmWave Networks},
author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/TBC.2021.3061979},
issn = {1557-9611},
year = {2021},
date = {2021-09-01},
journal = {IEEE Transactions on Broadcasting},
volume = {67},
number = {3},
pages = {593-605},
abstract = {Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {article}
}
Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe
Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks Proceedings Article
In: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, IEEE, 2021, ISBN: 978-1-6654-4909-0.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, wearables
@inproceedings{Chukhno2021b,
title = {Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks},
author = {Nadezhda Chukhno and Olga Chukhno and Sara Pizzi and Antonella Molinaro and Antonio Iera and Giuseppe Araniti},
doi = {10.1109/BMSB53066.2021.9547189},
isbn = {978-1-6654-4909-0},
year = {2021},
date = {2021-08-08},
booktitle = {2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting},
pages = {1-6},
publisher = {IEEE},
abstract = {The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a low-complexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem.},
keywords = {A-wear, machine learning, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a low-complexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem.
Ometov, Aleksandr; Shubina, Viktoriia; Klus, Lucie; Skibińska, Justyna; Saafi, Salwa; Pascacio-de-los-Santos, Pavel; Flueratoru, Laura; Quezada-Gaibor, Darwin; Chukhno, Nadezhda; Chukhno, Olga; Ali, Asad; Channa, Asma; Svertoka, Ekaterina; Qaim, Waleed Bin; Casanova-Marqués, Raúl; Holcer, Sylvia; Torres-Sospedra, Joaquín; Casteleyn, Sven; Ruggeri, Giuseppe; Araniti, Giuseppe; Burget, Radim; Hosek, Jiri; Lohan, Elena Simona
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges Journal Article
In: Computer Networks, vol. 193, pp. 108074, 2021, ISSN: 1389-1286.
Abstract | Links | BibTeX | Tags: A-wear, interoperability, Standards, wearables
@article{Ometov2021,
title = {A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges},
author = {Aleksandr Ometov and Viktoriia Shubina and Lucie Klus and Justyna Skibińska and Salwa Saafi and Pavel Pascacio-de-los-Santos and Laura Flueratoru and Darwin Quezada-Gaibor and Nadezhda Chukhno and Olga Chukhno and Asad Ali and Asma Channa and Ekaterina Svertoka and Waleed Bin Qaim and Raúl Casanova-Marqués and Sylvia Holcer and Joaquín Torres-Sospedra and Sven Casteleyn and Giuseppe Ruggeri and Giuseppe Araniti and Radim Burget and Jiri Hosek and Elena Simona Lohan},
doi = {https://doi.org/10.1016/j.comnet.2021.108074},
issn = {1389-1286},
year = {2021},
date = {2021-07-05},
journal = {Computer Networks},
volume = {193},
pages = {108074},
abstract = {Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.},
keywords = {A-wear, interoperability, Standards, wearables},
pubstate = {published},
tppubtype = {article}
}
Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.
Ometov, Aleksandr; Chukhno, Olga; Chukhno, Nadezhda; Nurmi, Jari; Lohan, Elena Simona
When wearable technology meets computing in future networks: a road ahead Proceedings Article
In: Proceedings of the 18th ACM International Conference on Computing Frontiers, pp. 185–190, ACM, 2021, ISBN: 9781450384049.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@inproceedings{Ometov2021a,
title = {When wearable technology meets computing in future networks: a road ahead},
author = {Aleksandr Ometov and Olga Chukhno and Nadezhda Chukhno and Jari Nurmi and Elena Simona Lohan},
doi = {https://doi.org/10.1145/3457388.3458614},
isbn = {9781450384049},
year = {2021},
date = {2021-05-01},
booktitle = {Proceedings of the 18th ACM International Conference on Computing Frontiers},
pages = {185–190},
publisher = {ACM},
abstract = {Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.
Pascacio-de-los-Santos, Pavel; Casteleyn, Sven; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari
Collaborative Indoor Positioning Systems: A Systematic Review Journal Article
In: Sensors, vol. 21, no. 3, pp. 1002, 2021.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning
@article{Pascacio-de-los-Santos2021,
title = {Collaborative Indoor Positioning Systems: A Systematic Review},
author = {Pavel Pascacio-de-los-Santos and Sven Casteleyn and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi},
doi = {https://doi.org/10.3390/s21031002 },
year = {2021},
date = {2021-02-03},
journal = {Sensors},
volume = {21},
number = {3},
pages = {1002},
abstract = {Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified},
keywords = {A-wear, Indoor positioning},
pubstate = {published},
tppubtype = {article}
}
Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified
Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Granell-Canut, Carlos; Talvitie, Jukka; Valkama, Mikko; Nurmi, Jari
Direct Lightweight Temporal Compression for Wearable Sensor Data Journal Article
In: IEEE Sensors Letters, vol. 5, no. 2, pp. 7000404, 2021, ISSN: 2475-1472.
Abstract | Links | BibTeX | Tags: A-wear, Sensors, wearables
@article{Klus2021,
title = {Direct Lightweight Temporal Compression for Wearable Sensor Data},
author = {Lucie Klus and Roman Klus and Elena Simona Lohan and Carlos Granell-Canut and Jukka Talvitie and Mikko Valkama and Jari Nurmi},
doi = {https://doi.org/10.1109/LSENS.2021.3051809 },
issn = {2475-1472},
year = {2021},
date = {2021-01-15},
journal = {IEEE Sensors Letters},
volume = {5},
number = {2},
pages = {7000404},
abstract = {Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-series data, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricted devices. Our method is based on the lightweight temporal compression method, targeting further reconstruction error minimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates the method's performance on several sensor-based, time-series data types. We prove that DLTC outperforms the considered benchmark methods in compression efficiency at the same reconstruction error level.
},
keywords = {A-wear, Sensors, wearables},
pubstate = {published},
tppubtype = {article}
}
Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-series data, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricted devices. Our method is based on the lightweight temporal compression method, targeting further reconstruction error minimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates the method's performance on several sensor-based, time-series data types. We prove that DLTC outperforms the considered benchmark methods in compression efficiency at the same reconstruction error level.
2020
Klus, Roman; Klus, Lucie; Solomitckii, Dmitrii; Talvitie, Jukka; Valkama, Mikko
Deep Learning-Based Cell-Level and Beam-Level Mobility Management System Journal Article
In: Sensors, vol. 20, no. 24, pp. 7124, 2020, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: A-wear, machine learning, urban mobility
@article{Klus2020a,
title = {Deep Learning-Based Cell-Level and Beam-Level Mobility Management System},
author = {Roman Klus and Lucie Klus and Dmitrii Solomitckii and Jukka Talvitie and Mikko Valkama},
doi = {https://doi.org/10.3390/s20247124},
issn = {1424-8220},
year = {2020},
date = {2020-12-11},
journal = {Sensors},
volume = {20},
number = {24},
pages = {7124},
abstract = {The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.},
keywords = {A-wear, machine learning, urban mobility},
pubstate = {published},
tppubtype = {article}
}
The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.
Chukhno, Olga; Chukhno, Nadezhda; Araniti, Giuseppe; Campolo, Claudia; Iera, Antonio; Molinaro, Antonella
Optimal Placement of Social Digital Twins in Edge IoT Networks Journal Article
In: Sensors, vol. 20, no. 21, pp. 6181, 2020, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: A-wear, digital twin, Internet of things
@article{Chukhno2020a,
title = {Optimal Placement of Social Digital Twins in Edge IoT Networks},
author = {Olga Chukhno and Nadezhda Chukhno and Giuseppe Araniti and Claudia Campolo and Antonio Iera and Antonella Molinaro},
doi = {https://doi.org/10.3390/s20216181},
issn = {1424-8220},
year = {2020},
date = {2020-10-30},
journal = {Sensors},
volume = {20},
number = {21},
pages = {6181},
abstract = {In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.},
keywords = {A-wear, digital twin, Internet of things},
pubstate = {published},
tppubtype = {article}
}
In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.
Chukhno, Nadezhda; Chukhno, Olga; Araniti, Giuseppe; Iera, Antonio; Molinaro, Antonella; Pizzi, Sara
Challenges and Performance Evaluation of Multicast Transmission in 60 GHz mmWave Proceedings Article
In: Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2020., pp. 3-17, Springer, Cham, 2020, ISBN: 978-3-030-66241-7.
Abstract | Links | BibTeX | Tags: A-wear
@inproceedings{Chukhno2020b,
title = {Challenges and Performance Evaluation of Multicast Transmission in 60 GHz mmWave},
author = {Nadezhda Chukhno and Olga Chukhno and Giuseppe Araniti and Antonio Iera and Antonella Molinaro and Sara Pizzi},
doi = {https://doi.org/10.1007/978-3-030-66242-4_1},
isbn = {978-3-030-66241-7},
year = {2020},
date = {2020-09-30},
booktitle = {Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2020.},
volume = {1337},
pages = {3-17},
publisher = {Springer, Cham},
abstract = {Recently, millimeter-wave (mmWave) technology has attracted significant attention due to its ambitious promise to deal with the rapid growth in wireless data traffic. Moreover, mmWave is expected to constitute a foundation for the fifth-generation (5G) communication systems’ services, claimed to efficiently and effectively support both unicast and multicast transmission modes. However, the use of highly directional antennas at both user and access point sides is required to compensate for the severe path loss, high attenuation, and atmospheric absorption at extremely high-frequency bands, e.g., mmWave. Hence, multicast transmission needs special attention in directional systems due to the nature of group-oriented services, wherein a single beam simultaneously feeds receivers located at different positions. Since the widest possible beams at 60 GHz band are limited in terms of range and data rate and cannot serve all users, and, inversely, the use of only fine beams steered toward each user in unicast fashion requires long data transmission duration, the design of efficient directional multicast schemes is of utmost importance. Further, a slight beam misalignment due to mobility can generate a significant signal drop even between devices communicating in unicast fashions. The mission of this paper is to discuss the main challenges that must be faced to take advantage of mmWave communication for multicast data delivery. To this end, we investigate the performance of such systems in terms of data rate and data transmission duration via simulations considering both static and dynamic scenarios.},
keywords = {A-wear},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, millimeter-wave (mmWave) technology has attracted significant attention due to its ambitious promise to deal with the rapid growth in wireless data traffic. Moreover, mmWave is expected to constitute a foundation for the fifth-generation (5G) communication systems’ services, claimed to efficiently and effectively support both unicast and multicast transmission modes. However, the use of highly directional antennas at both user and access point sides is required to compensate for the severe path loss, high attenuation, and atmospheric absorption at extremely high-frequency bands, e.g., mmWave. Hence, multicast transmission needs special attention in directional systems due to the nature of group-oriented services, wherein a single beam simultaneously feeds receivers located at different positions. Since the widest possible beams at 60 GHz band are limited in terms of range and data rate and cannot serve all users, and, inversely, the use of only fine beams steered toward each user in unicast fashion requires long data transmission duration, the design of efficient directional multicast schemes is of utmost importance. Further, a slight beam misalignment due to mobility can generate a significant signal drop even between devices communicating in unicast fashions. The mission of this paper is to discuss the main challenges that must be faced to take advantage of mmWave communication for multicast data delivery. To this end, we investigate the performance of such systems in terms of data rate and data transmission duration via simulations considering both static and dynamic scenarios.
Shubina, Viktoriia; 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
In: Data, vol. 5, no. 4, pp. 87, 2020, ISSN: 2306-5729.
Abstract | Links | BibTeX | Tags: A-wear, Internet of things, wearables
@article{Subina2020a,
title = {Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era},
author = {Viktoriia Shubina and Sylvia Holcer and Michael Gould and Elena Simona Lohan},
doi = {https://doi.org/10.3390/data5040087},
issn = {2306-5729},
year = {2020},
date = {2020-09-23},
journal = {Data},
volume = {5},
number = {4},
pages = {87},
abstract = {Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.},
keywords = {A-wear, Internet of things, wearables},
pubstate = {published},
tppubtype = {article}
}
Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Simona Elena; Nurmi, Jari; Huerta-Guijarro, Joaquín
Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Proceedings Article
In: Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event, pp. 208-213, 2020, ISBN: 978-1-7281-9281-9.
Links | BibTeX | Tags: A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping
@inproceedings{Quezada-Gaibor2020,
title = {Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices},
author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Simona Elena Lohan and Jari Nurmi and Joaquín Huerta-Guijarro},
doi = {http://www.doi.org/10.1109/ICUMT51630.2020.9222411},
isbn = {978-1-7281-9281-9},
year = {2020},
date = {2020-09-17},
booktitle = {Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event},
pages = {208-213},
keywords = {A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping},
pubstate = {published},
tppubtype = {inproceedings}
}
Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Simona Elena; Granell-Canut, Carlos; Nurmi, Jari
RSS Fingerprinting dataset size reduction using feature-wise adaptive k-means clustering. Proceedings Article
In: Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event,, pp. 195-200, 2020, ISBN: 978-1-7281-9281-9, (best paper ward).
Links | BibTeX | Tags: A-wear, Wi-Fi fingerprint
@inproceedings{Klus2020,
title = {RSS Fingerprinting dataset size reduction using feature-wise adaptive k-means clustering.},
author = {Lucie Klus and Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Simona Elena Lohan and Carlos Granell-Canut and Jari Nurmi},
doi = { http://www.doi.org/10.1109/ICUMT51630.2020.9222411},
isbn = {978-1-7281-9281-9},
year = {2020},
date = {2020-09-17},
booktitle = {Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event,},
pages = {195-200},
note = {best paper ward},
keywords = {A-wear, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán Martín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta-Guijarro, Joaquín
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Proceedings Article
In: 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020, pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-6455-7.
Abstract | Links | BibTeX | Tags: A-wear, Indoor positioning, Wi-Fi fingerprint
@inproceedings{Torres-Sospedra2020,
title = {New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting},
author = {Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Germán Martín Mendoza-Silva and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},
doi = {http://www.doi.org/10.1109/ICL-GNSS49876.2020.9115419 },
isbn = {978-1-7281-6455-7},
year = {2020},
date = {2020-07-02},
booktitle = {2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020},
pages = {1-6},
publisher = {IEEE},
abstract = {Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique
may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning
accuracy and a significantly better computational cost –around 40% lower– than the original k-means.},
keywords = {A-wear, Indoor positioning, Wi-Fi fingerprint},
pubstate = {published},
tppubtype = {inproceedings}
}
Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique
may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning
accuracy and a significantly better computational cost –around 40% lower– than the original k-means.
may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning
accuracy and a significantly better computational cost –around 40% lower– than the original k-means.
Holcer, Sylvia; Torres-Sospedra, Joaquín; Gould, Michael; Remolar, Inmaculada
Privacy in Indoor Positioning Systems:a systematic review Proceedings Article
In: 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020, pp. 1-6, IEEE 2020, ISBN: 978-1-7281-6455-7.
Abstract | Links | BibTeX | Tags: A-wear, geoprivacy
@inproceedings{Holcer2020,
title = {Privacy in Indoor Positioning Systems:a systematic review},
author = {Sylvia Holcer and Joaquín Torres-Sospedra and Michael Gould and Inmaculada Remolar},
doi = {http://www.doi.org/10.1109/ICL-GNSS49876.2020.9115496 },
isbn = {978-1-7281-6455-7},
year = {2020},
date = {2020-06-25},
booktitle = {2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020},
pages = {1-6},
organization = {IEEE},
abstract = {This article proposes a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently
private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on estimated user mobility cases},
keywords = {A-wear, geoprivacy},
pubstate = {published},
tppubtype = {inproceedings}
}
This article proposes a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently
private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on estimated user mobility cases
private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on estimated user mobility cases
Klus, Lucie; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari
Lossy Compression Methods for Performance-Restricted Wearable Devices Proceedings Article
In: Ometov, A.; Nurmi, Jari; Lohan, Elena Simona; Torres-Sospedra, Joaquín; (Eds), H. Kuusniemi (Ed.): Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020, CEUR, 2020, ISBN: 1613-0073.
Abstract | Links | BibTeX | Tags: A-wear, wearables
@inproceedings{Klus2019b,
title = {Lossy Compression Methods for Performance-Restricted Wearable Devices},
author = {Lucie Klus and Elena Simona Lohan and Carlos Granell-Canut and Jari Nurmi},
editor = {A. Ometov and Jari Nurmi and Elena Simona Lohan and Joaquín Torres-Sospedra and H. Kuusniemi (Eds) },
url = {http://ceur-ws.org/Vol-2626/paper9.pdf},
isbn = {1613-0073},
year = {2020},
date = {2020-06-12},
booktitle = {Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020},
volume = {2626},
publisher = {CEUR},
abstract = {With the increasing popularity, diversity, and utilization of wearable devices, the data transfer and storage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods.},
keywords = {A-wear, wearables},
pubstate = {published},
tppubtype = {inproceedings}
}
With the increasing popularity, diversity, and utilization of wearable devices, the data transfer and storage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods.
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
In: Internet of things, vol. 13, pp. 100334, 2020.
Abstract | Links | BibTeX | Tags: A-wear, Indoor localization
@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 = {A-wear, Indoor localization},
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
In: Data, vol. 5, no. 4, pp. 87, 2020.
Links | BibTeX | Tags: A-wear, geolocation, geoprivacy, mobi, Monitoring
@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 = {A-wear, geolocation, geoprivacy, mobi, Monitoring},
pubstate = {published},
tppubtype = {article}
}
2019
Klus, Lucie; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari
Crowdsourcing solutions for data gathering from wearables Conference
2019.
Abstract | Links | BibTeX | Tags: A-wear, crowdsourcing, data, wearables
@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 = {A-wear, crowdsourcing, data, wearables},
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.
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.
0000
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
In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 53-68, 0000, ISSN: 2832-7322.
Abstract | Links | BibTeX | Tags: A-wear, Autoencoder, Indoor positioning, Wi-Fi fingerprint
@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 = {A-wear, Autoencoder, Indoor positioning, Wi-Fi fingerprint},
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.