2023 |
Casanova-Marqués, Raúl; Torres-Sospedra, Joaquín; Hajny, Jan; Gould, Michael Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article Internet of Things, 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. |
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 IEEE Transactions on Mobile Computing, 22 (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. |
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. |
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 IEEE Transactions on Broadcasting, 69 (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 Inproceedings 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; Galinina, Olga; Andreev, Sergey; Gaidamaka, Yuliya; Samouylov, Konstantin; Araniti, Giuseppe A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks Journal Article IEEE Transactions on Wireless Communications , 21 (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 Inproceedings 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. |
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7, IEEE, 2022, ISBN: 978-1-7281-6218-8. Abstract | Links | BibTeX | Tags: geoprivacy, wearables @inproceedings{Bravenec2022a, title = {What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit}, author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza}, doi = {https://doi.org/10.1109/IPIN54987.2022.9918134}, isbn = {978-1-7281-6218-8}, year = {2022}, date = {2022-09-06}, booktitle = {2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages = {1-7}, publisher = {IEEE}, abstract = {The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11 th international conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization.}, keywords = {geoprivacy, wearables}, pubstate = {published}, tppubtype = {inproceedings} } The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11 th international conference on Indoor Positioning and Indoor Navigation (IPIN) 2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization. |
Dzurenda, Petr; Casanova-Marqués, Raúl; Malina, Lukas; Hajny, Jan Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials Inproceedings 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). |
Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices Inproceedings Artificial Intelligence in Medicine. AIME 2022, pp. 144-154, Springer, Cham, 2022, ISBN: 978-3031093418. Abstract | Links | BibTeX | Tags: machine learning, Mobile apps, mobile computing, symptoms, wearables @inproceedings{Matey2022a, title = {Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices}, author = {Miguel Matey-Sanz and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut}, doi = {https://doi.org/10.1007/978-3-031-09342-5_14}, isbn = {978-3031093418}, year = {2022}, date = {2022-07-09}, booktitle = {Artificial Intelligence in Medicine. AIME 2022}, volume = {13263}, pages = {144-154}, publisher = {Springer, Cham}, series = {Lectures Notes in Artificial Intelligence}, abstract = {Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test.}, keywords = {machine learning, Mobile apps, mobile computing, symptoms, wearables}, pubstate = {published}, tppubtype = {inproceedings} } Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test. |
2021 |
Chukhno, Nadezhda; Chukhno, Olga; Pizzi, Sara; Molinaro, Antonella; Iera, Antonio; Araniti, Giuseppe Efficient Management of Multicast Traffic in Directional mmWave Networks Journal Article IEEE Transactions on Broadcasting, 67 (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 Inproceedings 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 Computer Networks, 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 Inproceedings 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. |
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 IEEE Sensors Letters, 5 (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 |
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 Data, 5 (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 Inproceedings 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; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari Lossy Compression Methods for Performance-Restricted Wearable Devices Inproceedings Ometov, A; Nurmi, Jari; Lohan, Elena Simona; Torres-Sospedra, Joaquín; (Eds), Kuusniemi H (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. |
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. |
IF Journal
Journal
Book
Book chapter
Congress
Thesis & M. Thesis
2023 |
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs Journal Article Internet of Things, 22 , pp. 100801, 2023, ISSN: 2542-6605. |
Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas Journal Article IEEE Transactions on Mobile Computing, 22 (6), pp. 3572 - 3588, 2023, ISSN: 1558-0660. |
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. |
The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems Journal Article IEEE Transactions on Broadcasting, 69 (1), pp. 201-214, 2023, ISSN: 1557-9611. |
2022 |
Modeling Reconfigurable Intelligent Surfaces-aided Directional Communications for Multicast Services Inproceedings GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 5850-5855, IEEE, 2022, ISBN: 978-1-6654-3541-3. |
A Holistic Assessment of Directional Deafness in mmWave-Based Distributed 3D Networks Journal Article IEEE Transactions on Wireless Communications , 21 (9), pp. 7491 - 7505, 2022, ISSN: 1558-2248. |
Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis Inproceedings 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications , pp. 1-6, IEEE, 2022, ISBN: 978-1-6654-8054-3. |
2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7, IEEE, 2022, ISBN: 978-1-7281-6218-8. |
Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials Inproceedings Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1-9, ACM, 2022, ISBN: 9781450396707. |
Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices Inproceedings Artificial Intelligence in Medicine. AIME 2022, pp. 144-154, Springer, Cham, 2022, ISBN: 978-3031093418. |
2021 |
Efficient Management of Multicast Traffic in Directional mmWave Networks Journal Article IEEE Transactions on Broadcasting, 67 (3), pp. 593-605, 2021, ISSN: 1557-9611. |
Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks Inproceedings 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, IEEE, 2021, ISBN: 978-1-6654-4909-0. |
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges Journal Article Computer Networks, 193 , pp. 108074, 2021, ISSN: 1389-1286. |
When wearable technology meets computing in future networks: a road ahead Inproceedings Proceedings of the 18th ACM International Conference on Computing Frontiers, pp. 185–190, ACM, 2021, ISBN: 9781450384049. |
Direct Lightweight Temporal Compression for Wearable Sensor Data Journal Article IEEE Sensors Letters, 5 (2), pp. 7000404, 2021, ISSN: 2475-1472. |
2020 |
Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era Journal Article Data, 5 (4), pp. 87, 2020, ISSN: 2306-5729. |
Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices Inproceedings 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. |
Lossy Compression Methods for Performance-Restricted Wearable Devices Inproceedings Ometov, A; Nurmi, Jari; Lohan, Elena Simona; Torres-Sospedra, Joaquín; (Eds), Kuusniemi H (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. |
2019 |
Crowdsourcing solutions for data gathering from wearables Conference 2019. |