• Towards greener smart cities with machin

    From ScienceDaily@1:317/3 to All on Mon Jan 31 21:30:44 2022
    Towards greener smart cities with machine learning-based 'sleep
    schedules'

    Date:
    January 31, 2022
    Source:
    Shibaura Institute of Technology
    Summary:
    While cellular networks are the foundation of smart cities, they
    consume a lot of energy, enhancing global warming. Putting base
    stations (BSs) with low traffic to sleep saves energy but also
    reduces traffic prediction accuracy. In a new study, researchers
    address this trade-off using machine learning technique to switch
    off BSs based on their contribution to prediction accuracy. The
    new scheme reduces power consumption and demonstrates a prediction
    accuracy superior to benchmark schemes.



    FULL STORY ==========================================================================
    The concept of smart cities is founded on sophisticated cellular networks
    that would not only connect humans in the future but also humans
    to other smart devices. However, this would also require huge energy consumption. In the wake of climate change, this can make matters worse
    for our environment by increasing the greenhouse gas emissions. Thus,
    we not only need smart cities but also greener smart cities.


    ==========================================================================
    One way to address this issue is by switching off base stations (BSs),
    radio transmitters/receivers that serve as the hub of the local wireless network, when they have little to no traffic load. Laboratory testing
    has shown that active BSs consume as much as 60% of the maximum energy consumption even under no traffic load and switching them off can bring
    it down to 40%. However, there is a trade-off: putting BSs to sleep
    makes their traffic logs unavailable, which also reduces the accuracy
    of traffic prediction. Is there a way to avoid this compromise between
    accuracy and sustainability? The answer, according to a new study,
    seems to be "yes." The study, led by Professor Ryoichi Shinkuma from
    Shibaura Institute of Technology (SIT), Japan, and his colleagues,
    Associate Professor Kaoru Ota from Muroran Institute of Technology,
    Japan and Associate Professor Takehiro Sato from Kyoto University,
    Japan, proposed a novel scheme that not only reduced energy consumption
    but demonstrated a higher traffic prediction accuracy compared to the
    benchmark schemes! This paper was published in Volume 35, Issue 6 of
    the journal IEEE Network Magazine on November/December 2021.

    How did the researchers achieve this remarkable feat? Prof. Shinkuma
    explains, "We applied software defined network (SDN) and edge computing
    to a cellular network such that each BS is equipped with an SDN switch,
    and an SDN controller can turn off any BS according to the traffic
    prediction results. An edge server collects the traffic logs through the
    SDN switches and predicts traffic volume using machine learning (ML)."
    The ML method used by the researchers decided which BSs could be put into "sleep mode" based on the importance of their traffic logs in improving
    the prediction accuracy. Thus, BSs with low contribution to the accuracy
    for previous time slots were put to sleep at the next slot to save energy.

    To validate their scheme, the researchers used real-world mobile traffic
    data collected over two months and compared its performance against that
    of two benchmark schemes. To their delight, the new scheme outperformed
    the benchmark schemes in its robustness against reducing the number of
    active BSs and different BS sets.

    Could this study be a harbinger of greener cellular networks and smart
    cities? Prof. Shinkuma is optimistic. "By intelligently controlling the operation of BSs, renewable energy sources could be used to power future networks and, depending on the availability of renewable energy resource,
    the sleep schedules of the BSs can be determined," he speculates.

    ========================================================================== Story Source: Materials provided by
    Shibaura_Institute_of_Technology. Note: Content may be edited for style
    and length.


    ========================================================================== Journal Reference:
    1. Ryoichi Shinkuma, Naoki Kishi, Kaoru Ota, Mianxiong Dong,
    Takehiro Sato,
    Eiji Oki. Smarter Base Station Sleeping for Greener Cellular
    Networks.

    IEEE Network, 2021; 35 (6): 98 DOI: 10.1109/MNET.110.2100224 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/01/220131131920.htm

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