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 @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 = {},
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. |
Fryza, Tomas; Bravenec, Tomás; Kohl, Zdenek Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings Inproceedings 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA, pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-9835-9. Abstract | Links | BibTeX @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 = {},
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 @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 = {},
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 @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 = {},
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 IEEE Transactions on Broadcasting, 69 (1), pp. 201-214, 2023, ISSN: 1557-9611. Abstract | Links | BibTeX @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 = {},
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. |
