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Modeling the Total Energy Consumption of Mobile Network Services and Applications

Author

Listed:
  • Ming Yan

    (Faculty of Science and Technology, Communication University of China, Beijing 100024, China)

  • Chien Aun Chan

    (Networked Society Institute, University of Melbourne, Melbourne, VIC 3010, Australia)

  • André F. Gygax

    (Networked Society Institute, University of Melbourne, Melbourne, VIC 3010, Australia
    Department of Finance, Faculty of Business and Economics, University of Melbourne, Melbourne, VIC 3010, Australia)

  • Jinyao Yan

    (Faculty of Science and Technology, Communication University of China, Beijing 100024, China)

  • Leith Campbell

    (Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia)

  • Ampalavanapillai Nirmalathas

    (Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia)

  • Christopher Leckie

    (School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia)

Abstract

Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

Suggested Citation

  • Ming Yan & Chien Aun Chan & André F. Gygax & Jinyao Yan & Leith Campbell & Ampalavanapillai Nirmalathas & Christopher Leckie, 2019. "Modeling the Total Energy Consumption of Mobile Network Services and Applications," Energies, MDPI, vol. 12(1), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:184-:d:195469
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    References listed on IDEAS

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    1. Adriana Fernández-Fernández & Cristina Cervelló-Pastor & Leonardo Ochoa-Aday, 2017. "Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems," Energies, MDPI, vol. 10(12), pages 1-27, December.
    2. Greta Vallero & Margot Deruyck & Michela Meo & Wout Joseph, 2018. "Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.
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    Cited by:

    1. Nelly Condori-Fernandez & Patricia Lago & Miguel R. Luaces & Ángeles S. Places, 2020. "An Action Research for Improving the Sustainability Assessment Framework Instruments," Sustainability, MDPI, vol. 12(4), pages 1-25, February.
    2. Lin Shi & Katharine J. Mach & Sangwon Suh & Adam Brandt, 2022. "Functionality‐based life cycle assessment framework: An information and communication technologies (ICT) product case study," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 782-800, June.
    3. Oughton, Edward & Geraci, Giovanni & Polese, Michele & Shah, Vijay & Bubley, Dean & Blue, Scott, 2024. "Reviewing wireless broadband technologies in the peak smartphone era: 6G versus Wi-Fi 7 and 8," Telecommunications Policy, Elsevier, vol. 48(6).

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