We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.
{"title":"Experimental Evaluation Toward Mobility-Driven Model Integration Between Edges","authors":"Shota Ono, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, K. Sezaki","doi":"10.1109/CCNC51664.2024.10454772","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454772","url":null,"abstract":"We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"103 10","pages":"610-611"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454876
Ei Tanaka, Y. Kawamoto, Nei Kato, Masashi Iwabuchi, Riku Ohmiya, T. Murakami
Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.
{"title":"Frequency Resource Allocation for IRS-Aided Communication Using Beam Squint Approach","authors":"Ei Tanaka, Y. Kawamoto, Nei Kato, Masashi Iwabuchi, Riku Ohmiya, T. Murakami","doi":"10.1109/CCNC51664.2024.10454876","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454876","url":null,"abstract":"Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"63 8","pages":"1064-1065"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454670
Shunsuke Kamiwatari, Masaaki Ito, I. Kanno, Kengo Ando, Koji Ishibashi
In this paper, we present a novel approach to analog beam selection in cell-free massive multi-input multi-output (CF-mMIMO) systems employing hybrid beamforming (BF) over millimeter-wave (mmWave) channels. In conventional CF-mMIMO systems with hybrid BF, an analog beam is selected by a central processing unit (CPU) from a predefined codebook. This selection is based on the received power at each access point (AP) and the fairness among user equipment (UE). However, this conventional method disregards the potential impact of interference and heavily relies on digital BF to mitigate it. Consequently, when utilizing low-complexity digital BF techniques such as maximum ratio transmission (MRT), the system's performance experiences degradation. Our proposed beam selection method takes into account both the received power and the interference power, effectively mitigating the influence of interference while maintaining an adequate level of received power. The numerical simulations validate the efficacy of our proposed approach.
{"title":"Interference-Aware Analog Beam Selection for Cell-Free Massive MIMO With Hybrid Beamforming Over Millimeter-Wave Channels","authors":"Shunsuke Kamiwatari, Masaaki Ito, I. Kanno, Kengo Ando, Koji Ishibashi","doi":"10.1109/CCNC51664.2024.10454670","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454670","url":null,"abstract":"In this paper, we present a novel approach to analog beam selection in cell-free massive multi-input multi-output (CF-mMIMO) systems employing hybrid beamforming (BF) over millimeter-wave (mmWave) channels. In conventional CF-mMIMO systems with hybrid BF, an analog beam is selected by a central processing unit (CPU) from a predefined codebook. This selection is based on the received power at each access point (AP) and the fairness among user equipment (UE). However, this conventional method disregards the potential impact of interference and heavily relies on digital BF to mitigate it. Consequently, when utilizing low-complexity digital BF techniques such as maximum ratio transmission (MRT), the system's performance experiences degradation. Our proposed beam selection method takes into account both the received power and the interference power, effectively mitigating the influence of interference while maintaining an adequate level of received power. The numerical simulations validate the efficacy of our proposed approach.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"77 4","pages":"696-701"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454888
Uttam Ghosh, Debashis Das, Sourav Banerjee, S. Mohanty
The proliferation of interconnected devices in the era of the Internet of Things (IoT) has given rise to the need for robust device identity management and authentication mechanisms in cyber-physical systems (CPSs). Traditional centralized approaches to identity management face challenges of security, scalability, and privacy. Therefore, the paper provides an innovative approach by fusing Self-Sovereign Identity (SSI) with blockchain technology to revolutionize device identity management within CPS environments. In this paper, devices autonomously initiate their identity-creation processes. Each device generates a cryptographic key pair comprising a public key for openly identifying the device and a closely guarded private key used for authentication and decryption purposes. The research also introduces an innovative authentication algorithm within CPS environments that employs secure tokens to validate the authenticity of devices. The proposed framework reduces the risk of unauthorized access and data breaches while empowering devices with control over their identities. Overall, the proposed approach not only enhances security, privacy, and resilience within CPSs but also provides a transformative solution for identity management in dynamic and autonomous device environments.
{"title":"Blockchain-Based Device Identity Management and Authentication in Cyber-Physical Systems","authors":"Uttam Ghosh, Debashis Das, Sourav Banerjee, S. Mohanty","doi":"10.1109/CCNC51664.2024.10454888","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454888","url":null,"abstract":"The proliferation of interconnected devices in the era of the Internet of Things (IoT) has given rise to the need for robust device identity management and authentication mechanisms in cyber-physical systems (CPSs). Traditional centralized approaches to identity management face challenges of security, scalability, and privacy. Therefore, the paper provides an innovative approach by fusing Self-Sovereign Identity (SSI) with blockchain technology to revolutionize device identity management within CPS environments. In this paper, devices autonomously initiate their identity-creation processes. Each device generates a cryptographic key pair comprising a public key for openly identifying the device and a closely guarded private key used for authentication and decryption purposes. The research also introduces an innovative authentication algorithm within CPS environments that employs secure tokens to validate the authenticity of devices. The proposed framework reduces the risk of unauthorized access and data breaches while empowering devices with control over their identities. Overall, the proposed approach not only enhances security, privacy, and resilience within CPSs but also provides a transformative solution for identity management in dynamic and autonomous device environments.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"69 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454820
Khanh Nam Nguyen, Kenichi Takizawa
Camera-assisted prediction-based handover has been implemented in a proactive manner for an indoor dynamic 60 GHz radio channel. Here, our proposed deep learning model is utilized to forecast the decline in signal quality caused by blockage. The predictive model shows equivalent accuracy and faster training time in comparison with models using state-of-the-art deep learning architectures of ResNet-18 and ResNet-50, owing to its suitability to the data obtained in the proposed handover experiment. Accordingly, a proactive physical layer handover method is proposed, which is based on the link quality prediction. This method outperforms handover in a reactive man-ner, as evidenced by longer connected duration, demonstrating its feasibility of malntaining a seamless connection.
{"title":"Deep Learning-Based Proactive Physical Layer Handover using Cameras for Indoor Environment","authors":"Khanh Nam Nguyen, Kenichi Takizawa","doi":"10.1109/CCNC51664.2024.10454820","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454820","url":null,"abstract":"Camera-assisted prediction-based handover has been implemented in a proactive manner for an indoor dynamic 60 GHz radio channel. Here, our proposed deep learning model is utilized to forecast the decline in signal quality caused by blockage. The predictive model shows equivalent accuracy and faster training time in comparison with models using state-of-the-art deep learning architectures of ResNet-18 and ResNet-50, owing to its suitability to the data obtained in the proposed handover experiment. Accordingly, a proactive physical layer handover method is proposed, which is based on the link quality prediction. This method outperforms handover in a reactive man-ner, as evidenced by longer connected duration, demonstrating its feasibility of malntaining a seamless connection.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"3 1","pages":"364-367"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454815
Matthew Boeding, Paul Scalise, M. Hempel, H. Sharif
5th -Generation (5G) cellular networks enable a new approach to applications that require low latency. As part of 5G infrastructure, Ultra Reliable Low Latency Communication (URLLC) was defined to support low-latency services using small payloads. However, many Operational Technology (OT) protocols designed with latency in mind require larger payloads but allow for latencies that exceed URLLC's 1ms capability for their operation. An example of such protocols is IEC-61850 GOOSE, which mandates a maximum latency of 4ms, but often transports larger payloads. In this paper, we evaluate the latency implications of incorporating the GOOSE protocol over 5G connections. We evaluate network performance by measuring two different Intelligent Electronic Devices' contact closure times and compare those to measurements obtained from a standard GOOSE network setup. This analysis shows the impact on network latency and critical application performance, from which we can derive important network parameters to improve performance for private 5G-based OT network implementations.
{"title":"Evaluating the Latency Impact for Time-Critical Operational Technology Applications of Transitioning IEC-61850 GOOSE Operations to 5G","authors":"Matthew Boeding, Paul Scalise, M. Hempel, H. Sharif","doi":"10.1109/CCNC51664.2024.10454815","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454815","url":null,"abstract":"5th -Generation (5G) cellular networks enable a new approach to applications that require low latency. As part of 5G infrastructure, Ultra Reliable Low Latency Communication (URLLC) was defined to support low-latency services using small payloads. However, many Operational Technology (OT) protocols designed with latency in mind require larger payloads but allow for latencies that exceed URLLC's 1ms capability for their operation. An example of such protocols is IEC-61850 GOOSE, which mandates a maximum latency of 4ms, but often transports larger payloads. In this paper, we evaluate the latency implications of incorporating the GOOSE protocol over 5G connections. We evaluate network performance by measuring two different Intelligent Electronic Devices' contact closure times and compare those to measurements obtained from a standard GOOSE network setup. This analysis shows the impact on network latency and critical application performance, from which we can derive important network parameters to improve performance for private 5G-based OT network implementations.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"93 4","pages":"626-627"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454666
H. S. Rossi, Karan Mitra, C. Åhlund, Irina Cotanis, Niclas Örgen, Per Johansson
Mobile cloud gaming (MCG) lets users play cloud games (CG) on mobile devices anywhere via mobile networks. However, the stochastic nature of network quality of service (QoS) can result in varying user quality of experience (QoE). Understanding, modeling, and predicting the impact of mobile networks' QoS on users' QoE is crucial. This helps stakeholders optimize networks, and game developers efficiently create cloud-hosted games provisioned over mobile networks. This paper investigates the impact of QoS on users' QoE and proposes, develops and validates novel models for predicting QoE for MCG in mobile networks using realistic subjective tests. In particular, we propose and develop three QoE models using multiple, polynomial, and non-linear regression. Our results validate that multiple regression (with R2=0.79, RMSE=0.45) can model complex relationships between QoS factors that impact QoE. Multiple polynomial regression achieved the overall fit with (R2=0.94, RMSE=0.24). Lastly, the non-linear model achieved a good RMSE of 0.24. To select the best model out of the three, we applied the F-test and determined that polynomial regression had the best statistical fit.
{"title":"Objective QoE Models for Cloud-Based First Person Shooter Game over Mobile Networks","authors":"H. S. Rossi, Karan Mitra, C. Åhlund, Irina Cotanis, Niclas Örgen, Per Johansson","doi":"10.1109/CCNC51664.2024.10454666","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454666","url":null,"abstract":"Mobile cloud gaming (MCG) lets users play cloud games (CG) on mobile devices anywhere via mobile networks. However, the stochastic nature of network quality of service (QoS) can result in varying user quality of experience (QoE). Understanding, modeling, and predicting the impact of mobile networks' QoS on users' QoE is crucial. This helps stakeholders optimize networks, and game developers efficiently create cloud-hosted games provisioned over mobile networks. This paper investigates the impact of QoS on users' QoE and proposes, develops and validates novel models for predicting QoE for MCG in mobile networks using realistic subjective tests. In particular, we propose and develop three QoE models using multiple, polynomial, and non-linear regression. Our results validate that multiple regression (with R2=0.79, RMSE=0.45) can model complex relationships between QoS factors that impact QoE. Multiple polynomial regression achieved the overall fit with (R2=0.94, RMSE=0.24). Lastly, the non-linear model achieved a good RMSE of 0.24. To select the best model out of the three, we applied the F-test and determined that polynomial regression had the best statistical fit.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"15 3","pages":"550-553"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454788
T. Murakami, Naoki Aihara, Yu Tsukamoto, Akio Ikami, H. Shinbo, Yoshiaki Amano
We analyze the impact on the wireless quality of time division duplex Cell-Free massive multiple-input multiple-output (TDD CF-mMIMO) caused by phase variation in reference signals that modulate RF signals due to clock distribution over a radio access network (RAN). Phase variation in reference signals causes temporal phase variation due to temporal difference in transmission and reception in TDD. In addition, since APs are distributed among a RAN, phase variation also occurs due to clock distribution through fronthaul depending on the placement of reference clocks (RCs), which is a challenge for practical wide-area deployment. We propose RC distribution for practical user-centric RAN, considering use of IEEE 1588 or radio over fiber (RoF) technology. We analyze phase variation of reference signals and evaluate SINR of TDD CF-mMIMO by numerical simulations, revealing that clock distribution from distributed RCs with RoF secures high SINR.
{"title":"Analysis of Clock Distribution in User-centric Radio Access Network for Cell-Free Massive MIMO","authors":"T. Murakami, Naoki Aihara, Yu Tsukamoto, Akio Ikami, H. Shinbo, Yoshiaki Amano","doi":"10.1109/CCNC51664.2024.10454788","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454788","url":null,"abstract":"We analyze the impact on the wireless quality of time division duplex Cell-Free massive multiple-input multiple-output (TDD CF-mMIMO) caused by phase variation in reference signals that modulate RF signals due to clock distribution over a radio access network (RAN). Phase variation in reference signals causes temporal phase variation due to temporal difference in transmission and reception in TDD. In addition, since APs are distributed among a RAN, phase variation also occurs due to clock distribution through fronthaul depending on the placement of reference clocks (RCs), which is a challenge for practical wide-area deployment. We propose RC distribution for practical user-centric RAN, considering use of IEEE 1588 or radio over fiber (RoF) technology. We analyze phase variation of reference signals and evaluate SINR of TDD CF-mMIMO by numerical simulations, revealing that clock distribution from distributed RCs with RoF secures high SINR.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"106 1","pages":"815-818"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454736
Suzumi Sato, Hayato Shimano, Megumi Saito, Shigeru Shimamoto, N. Kobayashi
Smartphone can be used to operate IoT devices and access services such as smartphone game, but prolonged use cause gaming disorder. Gaming disorder was published as an international disease in 2022 by World Health Organization (WHO). Gaming disorder is a symptom of an inability to control the amount of time spent playing games daily. In recent years, the number of patients with gaming disorder has been increasing and there are not enough hospitals to treat them, so it is necessary to cure gaming disorder by oneself without visiting hospitals. Therefore, it is necessary to understand the stress that users are under while playing games, and to provide remedial measures such as encouraging rest within a reasonable range. Since this paper is an initial study, stress evaluation of users during smartphone game playing. To evaluate the stress using biometric information, we obtain temporal changes in radio wave intensity by reflecting radio waveform in the 2.4 GHz band to the chest, and pulse wave and respiration waveforms by signal processing, from which 17 parameters that are attributable to stress. From the one-month smartphone usage logs, 16 subjects with short average game time per month and 18 subjects with long average game time per month are extracted. Since it can be assumed that the stress state during game play differs between the groups with short and long average game time, comparison of the difference in variation during game play using stress-induced parameters and binary classification using machine learning to evaluate the user's stress state. Since this method uses two antennas in the 2.4GHz band, it will be possible to measure stress using smartphones in the future, contributing to the understanding of stress that is likely to be held unconsciously.
{"title":"Stress Evaluation with Biometric Information Using Smartphone by Radio Wave Reflection","authors":"Suzumi Sato, Hayato Shimano, Megumi Saito, Shigeru Shimamoto, N. Kobayashi","doi":"10.1109/CCNC51664.2024.10454736","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454736","url":null,"abstract":"Smartphone can be used to operate IoT devices and access services such as smartphone game, but prolonged use cause gaming disorder. Gaming disorder was published as an international disease in 2022 by World Health Organization (WHO). Gaming disorder is a symptom of an inability to control the amount of time spent playing games daily. In recent years, the number of patients with gaming disorder has been increasing and there are not enough hospitals to treat them, so it is necessary to cure gaming disorder by oneself without visiting hospitals. Therefore, it is necessary to understand the stress that users are under while playing games, and to provide remedial measures such as encouraging rest within a reasonable range. Since this paper is an initial study, stress evaluation of users during smartphone game playing. To evaluate the stress using biometric information, we obtain temporal changes in radio wave intensity by reflecting radio waveform in the 2.4 GHz band to the chest, and pulse wave and respiration waveforms by signal processing, from which 17 parameters that are attributable to stress. From the one-month smartphone usage logs, 16 subjects with short average game time per month and 18 subjects with long average game time per month are extracted. Since it can be assumed that the stress state during game play differs between the groups with short and long average game time, comparison of the difference in variation during game play using stress-induced parameters and binary classification using machine learning to evaluate the user's stress state. Since this method uses two antennas in the 2.4GHz band, it will be possible to measure stress using smartphones in the future, contributing to the understanding of stress that is likely to be held unconsciously.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"63 4","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454646
Luca Davoli, Laura Belli, Gianluigi Ferrari, Elisa Londero, Paolo Azzoni
Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.
{"title":"An Edge Computing-Oriented WoT Architecture for Air Quality Monitoring in Mobile Vehicular Scenarios","authors":"Luca Davoli, Laura Belli, Gianluigi Ferrari, Elisa Londero, Paolo Azzoni","doi":"10.1109/CCNC51664.2024.10454646","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454646","url":null,"abstract":"Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"43 6","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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