Towards greener smart cities with machine learning-based “sleep schedules”

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.

Will machine learning help us go green in our quest for smart cities? We cannot wait to see!

***

Reference

DOI: https://doi.org/10.1109/MNET.110.2100224

About Shibaura Institute of Technology (SIT), Japan

Shibaura Institute of Technology (SIT) is a private university with campuses in Tokyo and Saitama. Since the establishment of its predecessor, Tokyo Higher School of Industry and Commerce, in 1927, it has maintained “learning through practice” as its philosophy in the education of engineers. SIT was the only private science and engineering university selected for the Top Global University Project sponsored by the Ministry of Education, Culture, Sports, Science and Technology and will receive support from the ministry for 10 years starting from the 2014 academic year. Its motto, “Nurturing engineers who learn from society and contribute to society,” reflects its mission of fostering scientists and engineers who can contribute to the sustainable growth of the world by exposing their over 8,000 students to culturally diverse environments, where they learn to cope, collaborate, and relate with fellow students from around the world.

Website: https://www.shibaura-it.ac.jp/en/

About Professor Ryoichi Shinkuma from SIT, Japan

Ryoichi Shinkuma is a Professor at the Faculty of Computer Science and Engineering, Shibaura Institute of Technology, Japan, since 2021. He received his Ph.D. degree in communications from Osaka University, Japan, in 2003 and worked at Kyoto University as an assistant professor from 2003 to 2011 and as an associate professor from 2011 to 2021. His main research interest is cooperation in heterogeneous networks. He has published over 149 journal and conference articles and has been cited multiple times. He is also a recipient of several awards related to his work.

Funding Information

This study was funded in part by JST PRESTO Grant no. JPMJPR1854. The research results were partly obtained from research commissioned by the National Institute of Information and Communications Technology (NICT), Japan.