Energy Efficient Operation and Control of Green Base Stations with Renewable Energy: Theory to Practice, National Science Foundation
The annual global expenditure of electricity consumed is more than $10 billion dollars, with cellular base-stations contributing to 60-80% of the energy consumption. In order to reduce the energy requirements of wireless networks, a promising new approach is to connect transmitters (such as Base-Stations) with energy harvesting and storage devices. The key benefits of such base-stations are threefold: (i) Green base-stations are suited for deploying off-power-grid base-stations, or where reliable power does not exist; (ii) they will reduce the operational cost for network providers, which could translate to lower costs for end-customers; (iii) they will reduce the carbon emission footprint of the information infrastructure.
However, reducing energy consumption by itself is not the answer. The reason is that (i) renewal energy sources provide supply that can be sporadic and unpredictable, leading to potential interruptions in service; (2) energy efficiency can come at cost of throughput, increased delays, and stale information; and (3) neworks can become unreliable if they solely focus on energy reduction.
Thus, the major contribution of this work has been to develop green networks, that can adequately exploit these new harvesting and storage devices, and at the same time provide high end-user quality of service. The project has developed the mathematical foundations for the design and operation of wireless networks equipped with energy harvesting devices, and developed practical solutions that can be implemented in real systems from the device itself to large-scale network systems (terrestrial and drone based) and data centers systems.
The outcomes of the project were disseminated via talks at workshops, conferences, and presentations to universities and companies.
The importance of energy management in wireless networks, a major motivation for this work, has also been taught in a wireless networking course developed by the PI. This course is joint between CS and ECE. Each time the course is taught, at least one semester long project that the students can pick from emphasizes the need for designing wireless networks taking into account energy efficiency in practical settings.
Principal Investigators: Ness Shroff and Prasun Sinha (currently in industry)
Graduate Students and Postdocs
- Jiashang Liu (currently at Google)
- Ming Zhang (currently at Google)
- Fang Liu (currently at Facebook)
- Sinong Wang (currently at Facebook)
- Guidan Yao (Ohio State)
- Ahmed Bedewy
- Yang Yang (currently at Qualcomm)
- Tanmoy Das
- Yang Yang, Jiashang Liu, Prasun Sinha and Ness B. Shroff, “Dynamic User Association and Energy Control in Cellular Networks with Renewable Resources.” IEEE CDC, 2015.
- A. M. Bedewy, Y. Sun, and N. B. Shroff “Optimizing Data Freshness, Throughput, and Delay in Multi-Server Information-Update Systems,” IEEE ISIT'16, Barcelona, Spain, Jul., 2016.
- Jiashang Liu, Yang Yang, Prasun Sinha and Ness Shroff, “Load-Adaptive Base-Station Management for Energy Reduction including Operation-Cost and Turn-on-Cost,” Proc. of IEEE WCNC, San Francisco, 2017.
- Joohyun Lee, Kyunghan Lee*, Euijin Jeong, Jaemin Jo, and Ness B. Shroff, “CAS: Context-aware Background Application Scheduling in Interactive Mobile Systems,” IEEE Journal on Selected Areas in Communications - Special issue on Human-In-The-Loop Mobile Networks. 35 (5), 2017.
- Joohyun Lee, Kyunghan Lee, Euijin Jeong, Jaemin Jo, and Ness B. Shroff, “Context-aware Application Scheduling in Mobile Systems: What Will Users Do and Not Do Next?”, ACM UbiComp (Ubiquitous Computing), 2016.
- J. Lee, K. Lee, E. Jeong, J. Jo, and N. B. Shroff, “CAS: Context-aware Background Application Scheduling in Interactive Mobile Systems,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 35, no. 5, pp. 1013-1029, May 2017.
- Xiang Chen, Wei Chen, Joohyun Lee, and Ness B. Shroff (2017), “Delay-Optimal Buffer-Aware Scheduling with Adaptive Transmission,“ IEEE Trans. on Communications (TCOM), vol. 65, no. 7, pp. 2917-2930, July 2017.
- Xiang Chen, Wei Chen, Joohyun Lee, and Ness B. Shroff, “Delay-Optimal Probabilistic Scheduling in Green Communications with Arbitrary Arrival and Adaptive Transmission,” IEEE ICC, Paris, France, 2017.
- Y. Sun, E. Uysal-Biyikoglu, R. D. Yates, C. E. Koksal, and N. B. Shroff, “Update or Wait: How to Keep Your Data Fresh,” IEEE Trans. on Information Theory, vol. 63, no. 11, pp. 7492-7508, Nov. 2017.
- J. Liu, J. Lee, P. Sinha, and N. B. Shroff , “A Near-Optimal Control Policy in Cloud Systems with Renewable Sources and Time-dependent Energy Price. IEEE Cloud, Workshop Cloud Management and Operations,” 2018.
- M. Zhang, Z. Zheng, N. B. Shroff, “An Online Algorithm for Power-proportional Data Centers with Switching Cost,” IEEE CDC, 2018.
- S. Wang and N. B. Shroff (2018), "Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization,” ACM POMACS, 2018.
- Sinong Wang and Ness Shroff (2018), "Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization,” ACM Sigmetrics, 2018.
- J. Liu, W. Li, and N. B. Shroff (2019), "Energy Management for Timely Charging a System of Drones,” IEEE CDC, 2019.
- X. Zhao, W. Chen, J. Lee and N. B. Shroff, “Delay-Optimal and Energy-Efficient Communication with Markovian Arrivals,” IEEE Trans. on Communications (TCOM), vol. 68, no. 3, pp. 1508-1523, March 2020.
- A. M. Bedewy, Y. Sun, R. Singh and N. B. Shroff, “Optimizing Information Freshness using Low-Power Status Updates via Sleep-Wake Scheduling,” ACM MobiHoc’20, online, Oct. 2020.