IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v93y2015ip1p1210-1221.html
   My bibliography  Save this article

Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes

Author

Listed:
  • Premarathne, Uthpala Subodhani

Abstract

In smart homes, users remotely manage resource utilization tasks and context-aware services using portable devices and mobile communication technologies. Reliability of automated energy consumption management relies upon context-aware continuous authentications of users in executing time-critical tasks. In particular, the contexts of mobility of users and the critical nature of the task are important. Continuous authentication is a robust technique to ensure validity of the authenticity of users over time. Existing continuous authentication techniques do not use the contextual information and dynamic user behavioral characteristics for authentications. We propose a novel context-aware multi-attribute continuous authentication model for secure energy utilization management in smart homes. We use location and the critical nature of the tasks as the contextual information as supporting information for selecting the authentication attributes. We propose novel location and task profiles as context specification metrics and a novel relative-importance based attribute selection technique based on N-model. The usefulness of the proposed solution is validated using real-world data sets. Furthermore, the reliability of the proposed risk based resource management model is analysed as a constraint model using linear temporal logic. Based on the experimental results, this research provides meaningful insights to use pragmatic approaches with security and reliability assurances for resource management applications in smart homes.

Suggested Citation

  • Premarathne, Uthpala Subodhani, 2015. "Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes," Energy, Elsevier, vol. 93(P1), pages 1210-1221.
  • Handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:1210-1221
    DOI: 10.1016/j.energy.2015.09.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215012608
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2015.09.050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Arghira, Nicoleta & Hawarah, Lamis & Ploix, Stéphane & Jacomino, Mireille, 2012. "Prediction of appliances energy use in smart homes," Energy, Elsevier, vol. 48(1), pages 128-134.
    2. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2014. "Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments," Energy, Elsevier, vol. 69(C), pages 455-469.
    3. Balta-Ozkan, Nazmiye & Davidson, Rosemary & Bicket, Martha & Whitmarsh, Lorraine, 2013. "The development of smart homes market in the UK," Energy, Elsevier, vol. 60(C), pages 361-372.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    2. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    3. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    5. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    6. Wilson, Charlie & Hargreaves, Tom & Hauxwell-Baldwin, Richard, 2017. "Benefits and risks of smart home technologies," Energy Policy, Elsevier, vol. 103(C), pages 72-83.
    7. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Abosaq, Hamad & Alghamdi, Abdullah & Akib, Noor Adelyna Mohammed, 2022. "Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques," Technology in Society, Elsevier, vol. 71(C).
    9. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    10. Balta-Ozkan, Nazmiye & Davidson, Rosemary & Bicket, Martha & Whitmarsh, Lorraine, 2013. "The development of smart homes market in the UK," Energy, Elsevier, vol. 60(C), pages 361-372.
    11. Amel Attour & Marco Baudino & Jackie Krafft & Nathalie Lazaric, 2020. "Determinants of smart energy tracking application use at the city level: Evidence from France," Post-Print hal-02942483, HAL.
    12. Ebrahimzadeh Sarvestani, Maryam & Hoseiny, Saeed & Tavana, Davood & Di Maria, Francesco, 2024. "Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant," Energy, Elsevier, vol. 286(C).
    13. Hannah R. Marston & Joost van Hoof, 2019. "“Who Doesn’t Think about Technology When Designing Urban Environments for Older People?” A Case Study Approach to a Proposed Extension of the WHO’s Age-Friendly Cities Model," IJERPH, MDPI, vol. 16(19), pages 1-35, September.
    14. Hong, Jihoon & Shin, Jungwoo & Lee, Daeho, 2016. "Strategic management of next-generation connected life: Focusing on smart key and car–home connectivity," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 11-20.
    15. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    16. Hyo-Jin Kang & Jieun Han & Gyu Hyun Kwon, 2022. "The Acceptance Behavior of Smart Home Health Care Services in South Korea: An Integrated Model of UTAUT and TTF," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    17. Dongsu Kim & Yeobeom Yoon & Jongman Lee & Pedro J. Mago & Kwangho Lee & Heejin Cho, 2022. "Design and Implementation of Smart Buildings: A Review of Current Research Trend," Energies, MDPI, vol. 15(12), pages 1-17, June.
    18. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    19. Choi, Dae-Hyun & Xie, Le, 2016. "A framework for sensitivity analysis of data errors on home energy management system," Energy, Elsevier, vol. 117(P1), pages 166-175.
    20. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:1210-1221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.