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Publications (10 of 56) Show all publications
Nahar, N., Andersson, K., Schelén, O. & Saguna, S. (2024). A Survey on Zero Trust Architecture: Applications and Challenges of 6G Networks. IEEE Access, 12, 94753-94764
Open this publication in new window or tab >>A Survey on Zero Trust Architecture: Applications and Challenges of 6G Networks
2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 94753-94764Article in journal (Refereed) Published
Abstract [en]

As sixth-generation (6G) cellular networks emerge, promising unparalleled connectivity and capabilities, yet it amplifies concerns regarding security vulnerabilities. These networks include a broader array of devices and sensors compared to earlier generations, increasing the potential for attackers to exploit weaknesses. Existing security frameworks contribute to safeguarding enterprises against external threats that originate beyond the network perimeter. These frameworks operate under the assumption that all entities inside the defined perimeters are reliable, and their primary objective is to authorize access to resources based on assigned roles and permissions. However, this strategy could be more effective today since attacks might originate from any source, including within the network perimeter. To address this issue, a zero-trust architecture (ZTA) could be a potential solution that assumes neither users nor devices can be inherently trusted, and it consistently evaluates potential risks to decide whether to allow access to resources. This article will explore the zero-trust approach and its significance in contemporary network security. We describe the role of authentication and access control in ZTA and present an in-depth discussion of state-of-the-art authentication and access control techniques in different scenarios. This article examines the applicability of the zero-trust concept in 6G networks and analyzes the associated challenges and opportunities. This article also examines case studies demonstrating the practical application of the zero trust paradigm in 6G or comparable networks. It explores the research scope and tries to identify relevant research gaps in this area.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
6G mobile communication, 6G networks, Authentication, Computer architecture, Multi-factor authentication, Network security, Perimeter-based security, Security, Surveys, Zero Trust, Zero-trust architecture
National Category
Communication Systems Information Systems Telecommunications
Research subject
Cyber Security; Cyber-Physical Systems; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-108410 (URN)10.1109/ACCESS.2024.3425350 (DOI)001272140400001 ()2-s2.0-85198311694 (Scopus ID)
Funder
Interreg Aurora, 20357901
Note

Validerad;2024;Nivå 2;2024-07-25 (signyg);

Fulltext license: CC BY-NC-ND

Available from: 2024-07-25 Created: 2024-07-25 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
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 and Social Medicine
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: 2025-02-20Bibliographically 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
Shahid, Z. K., Saguna, S. & Åhlund, C. (2024). Variational Autoencoders for Anomaly Detection and Transfer Knowledge in Electricity and District Heating Consumption. IEEE transactions on industry applications, 60(5), 7437-7450
Open this publication in new window or tab >>Variational Autoencoders for Anomaly Detection and Transfer Knowledge in Electricity and District Heating Consumption
2024 (English)In: IEEE transactions on industry applications, ISSN 0093-9994, E-ISSN 1939-9367, Vol. 60, no 5, p. 7437-7450Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2024
Keywords
Anomaly Detection, Anomaly detection, Autoencoder, Buildings, CNNLSTM, District heating, Electricity, electricity and district heating Daily Consumption, Energy consumption, Resistance heating, RNN-LSTM, School buildings, transfer knowledge, VAE, Water heating
National Category
Energy Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-108412 (URN)10.1109/TIA.2024.3425805 (DOI)001319511900075 ()2-s2.0-85198352210 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-10-17 (joosat);

Available from: 2024-07-25 Created: 2024-07-25 Last updated: 2024-11-20Bibliographically approved
Mololoth, V. K., Åhlund, C. & Saguna, S. (2023). A Private Blockchain Based P2P Energy Trading Platform for Energy Users. In: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023: . Paper presented at 9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A Private Blockchain Based P2P Energy Trading Platform for Energy Users
2023 (English)In: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Series
Proceedings of the IEEE International Smart Cities Conference, ISSN 2687-8852, E-ISSN 2687-8860
National Category
Computer Sciences Energy Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103305 (URN)10.1109/ISC257844.2023.10293651 (DOI)2-s2.0-85178342222 (Scopus ID)979-8-3503-9775-8 (ISBN)979-8-3503-9776-5 (ISBN)
Conference
9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved
Kim, J. C., Saguna, S. & Åhlund, C. (2023). Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study. JMIR Human Factors, 10, Article ID e42145.
Open this publication in new window or tab >>Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study
2023 (English)In: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, article id e42145Article in journal (Refereed) Published
Abstract [en]

Background: The older population needs solutions for independent living and reducing the burden on caregivers while maintaining the quality and dignity of life.

Objective: The aim of this study was to design, develop, and evaluate an older adult health care app that supports trained caregivers (ie, formal caregivers) and relatives (ie, informal caregivers). We aimed to identify the factors that affect user acceptance of interfaces depending on the user’s role.

Methods: We designed and developed an app with 3 user interfaces that enable remote sensing of an older adult’s daily activities and behaviors. We conducted user evaluations (N=25) with older adults and their formal and informal caregivers to obtain an overall impression of the health care monitoring app in terms of user experience and usability. In our design study, the participants had firsthand experience with our app, followed by a questionnaire and individual interview to express their opinions on the app. Through the interview, we also identified their views on each user interface and interaction modality to identify the relationship between the user’s role and their acceptance of a particular interface. The questionnaire answers were statistically analyzed, and we coded the interview answers based on keywords related to a participant’s experience, for example, ease of use and usefulness.

Results: We obtained overall positive results in the user evaluation of our app regarding key aspects such as efficiency, perspicuity, dependability, stimulation, and novelty, with an average between 1.74 (SD 1.02) and 2.18 (SD 0.93) on a scale of −3.0 to 3.0. The overall impression of our app was favorable, and we identified that “simple” and “intuitive” were the main factors affecting older adults’ and caregivers’ preference for the user interface and interaction modality. We also identified a positive user acceptance of the use of augmented reality by 91% (10/11) of the older adults to share information with their formal and informal caregivers.

Conclusions: To address the need for a study to evaluate the user experience and user acceptance by older adults as well as both formal and informal caregivers regarding the user interfaces with multimodal interaction in the context of health monitoring, we designed, developed, and conducted user evaluations with the target user groups. Our results through this design study show important implications for designing future health monitoring apps with multiple interaction modalities and intuitive user interfaces in the older adult health care domain.

Place, publisher, year, edition, pages
JMIR Publications, 2023
Keywords
Internet of Things, health monitoring, older adults, augmented reality, user experience, independent living, design study, mobile phone
National Category
Human Computer Interaction
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-95822 (URN)10.2196/42145 (DOI)001017203700025 ()36884275 (PubMedID)2-s2.0-85149873927 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-08-10 (joosat);

Funder: Swedish Governmental Agency for Innovation Systems (grant 2017-02807)

Licens fulltext: CC BY License

Available from: 2023-03-09 Created: 2023-03-09 Last updated: 2024-10-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8561-7963

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