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Publications (10 of 123) Show all publications
Souza Rossi, H., Mitra, K., Åhlund, C. & Cotanis, I. (2024). A Demonstration of ALTRUIST for Conducting QoE Subjective Tests in Immersive Systems. In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC): . Paper presented at 21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024 (pp. 1120-1121). IEEE
Open this publication in new window or tab >>A Demonstration of ALTRUIST for Conducting QoE Subjective Tests in Immersive Systems
2024 (English)In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), IEEE, 2024, p. 1120-1121Conference paper, Oral presentation with published abstract (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
Series
Consumer Communications and Networking Conference, CCNC IEEE
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-104678 (URN)10.1109/CCNC51664.2024.10454751 (DOI)001192142600265 ()2-s2.0-85189198497 (Scopus ID)
Conference
21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024
Note

ISBN for host publication: 979-8-3503-0457-2

Available from: 2024-03-19 Created: 2024-03-19 Last updated: 2024-11-20Bibliographically approved
Vasquez Torres, M., Shahid, Z., Mitra, K., Saguna, S. & Åhlund, C. (2024). A Transfer Learning Approach to Create Energy Forecasting Models for Building Fleets. In: 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm): . Paper presented at 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), September 17-20, 2024, Oslo, Norway (pp. 438-444). IEEE
Open this publication in new window or tab >>A Transfer Learning Approach to Create Energy Forecasting Models for Building Fleets
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2024 (English)In: 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2024, p. 438-444Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
Series
IEEE International Conference on Smart Grid Communications, ISSN 2373-6836, E-ISSN 2474-2902
Keywords
Building fleet, Energy consumption, Transfer learning, LSTM, DTW, Hierarchical clustering, Time series forecasting
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-110870 (URN)10.1109/SmartGridComm60555.2024.10738094 (DOI)
Conference
2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), September 17-20, 2024, Oslo, Norway
Note

ISBN for host publication: 979-8-3503-1855-5;

Funder: European Commission (grant number 610619-EPP-1-2019-1-FREPPKA1-JMD-MOB), (EMJMD GENIAL Project);

Available from: 2024-11-28 Created: 2024-11-28 Last updated: 2024-11-28Bibliographically approved
Mololoth, V. K., Saguna, S. & Åhlund, C. (2024). Consensus algorithm for energy applications: Case study on P2P energy trading scenario. In: Rajiv Ranjan; Karan Mitra; Prem Prakash Jayaraman; Albert Y. Zomaya (Ed.), Managing Internet of Things Applications across Edge and Cloud Data Centres: (pp. 277-287). Institution of Engineering and Technology
Open this publication in new window or tab >>Consensus algorithm for energy applications: Case study on P2P energy trading scenario
2024 (English)In: Managing Internet of Things Applications across Edge and Cloud Data Centres / [ed] Rajiv Ranjan; Karan Mitra; Prem Prakash Jayaraman; Albert Y. Zomaya, Institution of Engineering and Technology , 2024, p. 277-287Chapter in book (Other academic)
Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2024
National Category
Energy Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-108686 (URN)10.1049/PBPC027E_ch12 (DOI)2-s2.0-85197680472 (Scopus ID)
Note

ISBN for host publication: 978-1-78561-779-9; 978-1-78561-780-5

Available from: 2024-08-22 Created: 2024-08-22 Last updated: 2024-11-13Bibliographically approved
Shahid, Z., Saguna, S., Åhlund, C. & Mitra, K. (2024). Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes. In: SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing: . Paper presented at The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain. (pp. 533-535). New York, NY, USA,: ACM Special Interest Group on Applied Computing
Open this publication in new window or tab >>Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes
2024 (English)In: SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, New York, NY, USA,: ACM Special Interest Group on Applied Computing , 2024, p. 533-535Conference paper, Published paper (Refereed)
Abstract [en]

One of the main concerns regarding the facilitation of the elderly well-being monitoring system is to preserve the participants’ privacy, enable the older adults to live longer independently, and support caregivers. Human Activity Recognition (HAR) in smart homes allows us to foresee the residents’ needs by identifying changes in behaviour that might link to possible health conditions. We propose a Federated learning (FL) model within the health monitoring application to generalize for diverse participant populations and to achieve comparable performance without disclosing the raw data to the traditional centralized approach, which raises privacy issues. In this study, we evaluate an unsupervised variational autoencoder (VAE) in centralized, individualized, compared to federated learning settings to learn the features of normal patterns of daily activities and build an anomaly detector based on reconstructed error resulting as outcomes by the trained model. Further, we validated our proposed approach on real-world datasets collected over three years from six single-resident elderly households. The individual and centralized-based learning models were used as a baseline to compare with FL. Our results show that the personalized FedAvg models achieve RMSE of about 1%, while the Global FL models achieve RMSE of approximately. 4%. The centralized model achieves RMSE of about 0.5%, and the RMSE of individual models based on local training ranges between 1% to 6%. The FL models are relatively comparable to the centralized baseline model.

Place, publisher, year, edition, pages
New York, NY, USA,: ACM Special Interest Group on Applied Computing, 2024
Keywords
Applied computing, Health informatics, Computing methodologies, Neural networks, Anomaly detection
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103826 (URN)10.1145/3605098.3636163 (DOI)001236958200079 ()2-s2.0-85197662033 (Scopus ID)
Conference
The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.
Note

ISBN for host publication: (979-8-4007-0243-3)

Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-10-08Bibliographically approved
Shahid, Z. K., Saguna, S. & Åhlund, C. (2024). Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes. IEEE Internet of Things Journal, 11(3), 4414-4429
Open this publication in new window or tab >>Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes
2024 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 11, no 3, p. 4414-4429Article in journal (Refereed) Published
Abstract [en]

Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Anomaly detection, Elderly healthcare, Internet of Things, Medical services, Motion detection, Multi-Armed bandits, Older adults, Reinforcement learning, Sensors, Sleep, Sleep patterns, Smart homes
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-99666 (URN)10.1109/JIOT.2023.3300015 (DOI)001166992300055 ()2-s2.0-85166780685 (Scopus ID)
Projects
FraViVo—Framtidens Välfärdsteknik med Internet of Things
Funder
Vinnova, 2020-04096
Note

Validerad;2024;Nivå 2;2024-03-18 (hanlid);

Full text license: CC BY-NC-ND

Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2024-11-20Bibliographically approved
Souza Rossi, H., Mitra, K., Åhlund, C., Cotanis, I., Örgen, N. & Johansson, P. (2024). Objective QoE Models for Cloud-Based First Person Shooter Game over Mobile Networks. In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC): . Paper presented at 21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024 (pp. 550-553). IEEE
Open this publication in new window or tab >>Objective QoE Models for Cloud-Based First Person Shooter Game over Mobile Networks
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2024 (English)In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), IEEE, 2024, p. 550-553Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
Series
Consumer Communications and Networking Conference, CCNC IEEE
National Category
Media and Communication Technology Communication Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-104680 (URN)10.1109/CCNC51664.2024.10454666 (DOI)001192142600088 ()2-s2.0-85189199208 (Scopus ID)
Conference
21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024
Note

ISBN for host publication: 979-8-3503-0457-2

Available from: 2024-03-19 Created: 2024-03-19 Last updated: 2024-11-20Bibliographically approved
Souza Rossi, H., Mitra, K., Åhlund, C. & Cotanis, I. (2024). QoE Models for Virtual Reality Cloud-based First Person Shooter Game over Mobile Networks. In: 2024 20th International Conference on Network and Service Management (CNSM) (CNSM 2024): . Paper presented at 2024 20th International Conference on Network and Service Management (CNSM)(CNSM 2024).
Open this publication in new window or tab >>QoE Models for Virtual Reality Cloud-based First Person Shooter Game over Mobile Networks
2024 (English)In: 2024 20th International Conference on Network and Service Management (CNSM) (CNSM 2024), 2024Conference paper, Published paper (Refereed)
Abstract [en]

Virtual reality cloud-based gaming (VRCG) services are becoming widely available on virtual reality (VR) devices delivered over computer networks.VRCG brings users worldwide an extensive catalog of games to play anywhere and anytime. Delivering these gaming services in existing broadband mobile networks is challenging due to their stochastic nature and the user perceived Quality of Experience (QoE)' sensitivity towards them. More research is needed regarding developing effective methods to measure the impact of network QoS factors on users' QoE in the VRCG context. Therefore, this paper proposes, develops, and validates three novel regression models trained on a real dataset collected via subjective tests (N=30); the dataset contains subjective users' QoE ratings regarding VR shooters affected by network conditions (N=28), such as round-trip time (RTT), random jitter (RJ), and packet loss (PL). Our findings reveal that due to the nonlinear relationship of (RTT and RJ) tested together, nonlinear(mean absolute error (MAE)=0.14) and polynomial (MAE=0.15) regression models have the best performance; yet, simple linear regression model(MAE=0.19) is also suitable to predict QoE for VRCG. Further, we found that the model's most important feature is RTT, followed by (RTT, RJ). Finally, our models' prediction of QoE for real-world traffic measurements suggests that mobile network traffic (4G, 5G non-standalone, 5G standalone) provides a 2.5 \(\leq MOS\_{QoE} \leq\) 3.0 experience for VRCG, while 4.2 \(\leqMOS\_{QoE} \leq\) 4.4  for wired connections, suggesting the need for improvements in the current commercial 5G network deployments to deliverVRCG.

Keywords
QoE, Cloud-based streaming, Virtual Reality, Metaverse, Games
National Category
Computer Sciences Communication Systems Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-109768 (URN)
Conference
2024 20th International Conference on Network and Service Management (CNSM)(CNSM 2024)
Available from: 2024-09-08 Created: 2024-09-08 Last updated: 2024-09-08
Shahid, Z. K., Saguna, S. & Åhlund, C. (2024). Recognizing Seasonal Sleep Patterns of Elderly in Smart Homes Using Clustering. In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC): . Paper presented at 21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024 (pp. 490-498). IEEE
Open this publication in new window or tab >>Recognizing Seasonal Sleep Patterns of Elderly in Smart Homes Using Clustering
2024 (English)In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), IEEE, 2024, p. 490-498Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Engineering and Technology Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103827 (URN)10.1109/CCNC51664.2024.10454817 (DOI)001192142600079 ()2-s2.0-85189207183 (Scopus ID)
Conference
21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024
Note

ISBN for host publication: 979-8-3503-0457-2;

Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-11-20Bibliographically approved
Souza Rossi, H., Mitra, K., Larsson, S., Åhlund, C. & Cotanis, I. (2024). Subjective QoE Assessment for Virtual Reality Cloud-based First-Person Shooter Game. In: Matthew Valenti; David Reed; Melissa Torres (Ed.), ICC 2024 - IEEE International Conference on Communications: . Paper presented at IEEE International Conference on Communications (ICC 2024), June 9-13, 2024, Denver, USA (pp. 4698-4703). IEEE
Open this publication in new window or tab >>Subjective QoE Assessment for Virtual Reality Cloud-based First-Person Shooter Game
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2024 (English)In: ICC 2024 - IEEE International Conference on Communications / [ed] Matthew Valenti; David Reed; Melissa Torres, IEEE, 2024, p. 4698-4703Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
Series
IEEE International Conference on Communications, E-ISSN 1938-1883
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-108929 (URN)10.1109/ICC51166.2024.10622467 (DOI)2-s2.0-85202845299 (Scopus ID)978-1-7281-9054-9 (ISBN)
Conference
IEEE International Conference on Communications (ICC 2024), June 9-13, 2024, Denver, USA
Available from: 2024-08-23 Created: 2024-08-23 Last updated: 2024-11-20Bibliographically approved
Kim, J. C., Saguna, S. & Åhlund, C. (2024). The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App. Applied Sciences, 14(7), Article ID 2621.
Open this publication in new window or tab >>The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
2024 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 14, no 7, article id 2621Article in journal (Refereed) Published
Abstract [en]

As the impact of global warming on climate change becomes noticeable, the importance of energy efficiency for reducing greenhouse gas emissions grows immense. To this end, a platform, solution, and mobile apps are developed as part of the European Union’s Horizon 2020 research and innovation program to support energy optimization in residences. However, to ensure long-term energy optimization, it is crucial to keep users engaged with the apps. Since augmented reality (AR) and a virtual animal companion positively influenced user engagement, we designed an AR companion that represented the user’s residence states; thereby making the user aware of indoor information. We conducted user evaluations to determine the effect of the AR companion on user engagement and perceived usability in the context of energy management. We identified that the user interface (UI) with AR (ARUI) barely affected user engagement and perceived usability compared to the traditional UI without AR (TUI); however, we found that the ARUI positively affected one of the user engagement aspects. Our results show AR companion integration’s potential benefits and effects on energy management mobile apps. Furthermore, our findings provide insights into UI design elements for developers considering multiple interaction modalities with AR.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
user interface, user evaluation, user engagement, perceived usability, augmented reality, Internet of Things, energy management
National Category
Human Computer Interaction
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-104850 (URN)10.3390/app14072671 (DOI)001201100700001 ()2-s2.0-85192566624 (Scopus ID)
Funder
EU, Horizon 2020, 893079
Note

Validerad;2024;Nivå 2;2024-03-22 (signyg);

Full text license: CC BY

Available from: 2024-03-22 Created: 2024-03-22 Last updated: 2024-10-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8681-9572

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