Energy Efficiency in Large-scale Internet of Things Networks

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Journal Title
Journal ISSN
Volume Title
School of Science | Doctoral thesis (article-based) | Defence date: 2018-06-19
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
64 + app. 54
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 111/2018
Abstract
Near-ubiquitous wireless connectivity combined with advancements in hardware and battery technology have enabled a proliferation of Internet-connected consumer and industrial devices. From smartphones in users' hands to Internet of Things (IoT) nodes streaming data from factory equipment, these devices have enabled new categories of services that have become a vital part of the consumer and industrial markets. Although various hardware and software aspects of wireless devices have seen immense improvements, battery technology still remains on a linear improvement path and optimization of the physical layer of the network stack is giving diminishing returns. Thus, the need to optimize the energy usage of the whole network stack still remains. This thesis focuses on the challenges related to understanding the effect of network traffic transmissions on the energy usage of the device. Specifically, research questions posited in this thesis look at the short-term predictability of user-driven network traffic and whether it can be exploited for traffic scheduling. Also, we explore what other variables affect energy usage during network transmissions and how they can be used in activity and energy models, which are needed to create less intrusive, leaner and more scalable energy profiling tools. For our research work, we chose two example scenarios - Android smartphones and IoT motes on a IEEE 802.15.4 network - each representing one of our focus networks.
Description
Supervising professor
Ylä-Jääski, Antti, Prof., Aalto University, Department of Computer Science, Finland
Thesis advisor
Ou, Zhonghong, Prof., Beijing University of Posts and Telecommunications, China
Xiao, Yu, Prof., Aalto University, Department of Communications and Networking, Finland
Keywords
internet of things, energy efficiency, energy modeling
Other note
Parts
  • [Publication 1]: Vilen Looga, Zhonghong Ou, Yu Xiao, Antti Ylä-Jääski. The Great Expectations of Smartphone Traffic Scheduling. In The Twentieth IEEE Symposium on Computers and Communications (ISCC 2015), Larnaca, pages 828-834, July 2015.
    DOI: 10.1109/ISCC.2015.7405616 View at publisher
  • [Publication 2]: Vilen Looga, Xiao Yu, Zhonghong Ou, Antti Ylä-Jääski. Exploiting Traffic Scheduling Mechanisms to Reduce Transmission Cost on Mobile Devices. In 2012 IEEE Wireless Communications and Networking Conference (WCNC 2012), Paris, pages 1766-1770, April 2012.
    DOI: 10.1109/WCNC.2012.6214070 View at publisher
  • [Publication 3]: Vilen Looga, Zhonghong Ou, Yang Deng, Antti Ylä-Jääski. Inference-based Energy Modeling in Large-scale Internet of Things Testbeds. In IEEE Sensors Journal (Submitted for review), 12 pages, January 2018
  • [Publication 4]: Vilen Looga, Zhonghong Ou, Yang Deng, Antti Ylä-Jääski. PowerShark: IEEE 802.15.4 Mote Activity Analysis Using Power Traces and Neural Networks. In 2016 IEEE Global Communications Conference (GLOBECOM 2016), Washington DC, pages 1-7, December 2016.
    DOI: 10.1109/GLOCOM.2016.7842163 View at publisher
  • [Publication 5]: Vilen Looga, Zhonghong Ou, Yang Deng, Antti Ylä-Jääski. MAMMOTH: A Massive-scale Emulation Platform for Internet of Things. In 2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS 2012), Hangzhou, pages 1235-1239, October 2012.
    DOI: 10.1109/CCIS.2012.6664581 View at publisher
Citation