| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | July 15, 2016 |
| Latest Amendment Date: | July 15, 2016 |
| Award Number: | 1619184 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alhussein Abouzeid
aabouzei@nsf.gov (703)292-7855 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2016 |
| End Date: | September 30, 2020 (Estimated) |
| Total Intended Award Amount: | $250,000.00 |
| Total Awarded Amount to Date: | $250,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
9500 GILMAN DR LA JOLLA CA US 92093-0021 (858)534-4896 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
La Jolla CA US 92093-0934 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Networking Technology and Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The massive increases in wireless devices and data are expected to lead to significant increases in energy consumption and carbon emissions of future wireless networks. The problem will be exacerbated by a growing number of off-grid base stations powered by diesel. This project addresses the sustainability of future wireless networks by significantly reducing the use of electricity and diesel in running wireless base stations, and thereby carbon emissions. While various techniques have been proposed to reduce power consumption of wireless networks, this project will address the challenging problem of efficient and cost-effective use of intermittent renewable power sources like solar and wind power to minimize grid/diesel power consumption while ensuring no adverse impact on user experience. Additionally, the project will demonstrate the feasibility of solar-powered small cells, significantly enhancing wireless connectivity in rural or remote areas, and enable unplanned and rapid deployments in urban areas. The resulting software and testbed will facilitate future research in renewable energy for wireless networks, and a new course on sustainable communications. The PI will work closely with University of California-San Diego Center for Wireless Communications industry members to validate the techniques developed, and facilitate adoption in their products which will lead to adoption of solar and wind energy sources to power future generations of wireless networks.
The project will introduce the concept of using data storage in user devices to transfer surplus renewable power to surplus data stored, to be utilized during periods of deficit renewable power to reduce electricity/diesel, with no need for energy storage. It will be enabled by a novel dynamic base station resource allocation technique which can indirectly impact data rate. This, in turn, will affect the data stored at user devices and also the base station power consumed, depending on surplus and deficit periods, without affecting user experience. A second technique will be developed for use of an additional low capacity energy storage at the base station to further enhance utilization of the temporally varying renewable power. The technique will simultaneously decide to use project will also develop new dynamic user association and transmit power adaptation techniques making use of spatial variations in renewable power between neighboring base stations to minimize total grid electricity/diesel consumed for heterogeneous networks, including showing feasibility of solar-only powered small cells. A simulation framework and testbed will be developed to demonstrate the effectiveness of the approaches.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
In order to address the challenges of increasing power consumption in cellular network and hence, high volume of carbon dioxide equivalent (CO2e) emission due to the explosive growth in mobile data traffic, in this project we focussed on base station (BS) utilizing harvested renewable energy (RE). Instead of traditional BS power minimization techniques, we developed techniques to optimize the utilization of RE, which may increase the BS power consumption while reducing the grid power consumption. To address the mismatch between fluctuating harvested RE at the BS, and its power consumption requirement due to the mobile traffic demand, we proposed solutions to 1) grid powered BS: adapting BS resource allocation and user association among neighboring BSs, according to the geographic distribution of harvested RE and traffic demand by UEs in heterogeneous networks comprising of small cells and macro BSs, and 2) off-grid small cell BS: adapting computing and communication resource allocation, user association, and application offloading among users and small cell BSs to ensure the QoS for services relying on the small cell BSs. We also developed a mathematical and simulation-based framework as well as a prototype and testbed to provide feasibility and performance analysis.
Specifically, the major technical outcomes and accomplishments include:
1. Considering grid powered BS, we developed a Lyapunov-based solar power-aware BS resource (L-SPAR) allocation technique to decide on the optimal use of mobile user?s data buffer and BSs? energy storage to minimize grid power consumption. The proposed approach is applicable to any application that utilizes data buffer of users.
2. To ensure broader impact, we considered an evolving use case for future connected and autonomous vehicles, where roadside units (RSUs), consisting of small cells with vehicular edge computing (VEC) servers, are expected to support the computation and communication needs of latency sensitive advanced vehicular applications. We consider off-grid and solar-powered RSUs, and proposed a Two-Phase QoS loss Minimization Algorithm (TQMA) to determine optimal user association and allocation of RSUs? computing and communication resources and energy storage to minimize the loss of QoS for vehicle users served by RSUs. The decision making of TQMA can be achieved in real-time to accommodate the fast-changing vehicular traffic pattern.
3. We expanded the above application offloading scenario by taking the vehicle?s local computing resource into consideration. We proposed a Dynamic offloading User association and Resource allocation for QoS loss minimization (DURQ) algorithm, which ensures minimal loss of QoS through dynamic task partitioning and offloading considering local and edge computing resources.
4. We improved the task partitioning and offloading technique by taking the system and application-level adaptation into consideration. We proposed System and Application aware Multi-user Offloading Algorithm (SAMOA), which jointly minimizes the end-to-end delay and maximizes the accuracy of the offloaded vehicular applications by dynamically changing task partitioning and offloading, edge server?s system configurations, and appropriate image compression level.5. Our research demonstrated the feasibility of solely using renewable energy to provide high quality of service for mobile users with extensive communication traffic and computing loads.
6. We developed a RE-powered RSU prototype and testbed to help validate our approach through real-world measurements. Based on data collected using the testbed, we developed machine learning based models to predict the State-of-Energy (SOE) of RE-RSUs, which allows advanced planning for optimal configuration, deployment and use of such RSUs to satisfy predicted vehicular traffic loads, informed by solar and/or wind generation profiles for the RSUs.
The broader impacts of the project include:
- The project facilitated the training of 2 PhD students and two MS students, including one female student, in the emerging areas of green wireless communications, vehicular computing, smart transportation infrastructure and artificial intelligence.
- The project demonstrated the feasibility and efficiency of the use of renewable energy for wireless communication infrastructure, including macro base-stations and small cells, with implications on not only lowering carbon footprint of increasing wireless deployments but also enhanced connectivity for remote and rural regions using RE-driven small cells.
- The development of the RE-powered RSU prototype testbed, which can be operated and accessed remotely, can have significant impact on future research, testing and commercial development of (a) smart RSU technologies which can support emerging vehicular applications, and (b) renewable energy research and development for edge computing, small cell densification and intelligent transportation systems (ITS) infrastructure. Because of remote configurability and accessibility, the RE-RSU testbed can be potentially utilized for research and commercialization activities nationally.
- The project findings have also been broadly disseminated through conference presentations, journal publications, forums, posters, and our website: http://mesdat.ucsd.edu/projects/green-communications. The project has also led to collaborations with local cleantech company and organization PrimoWind and CleanTech San Diego respectively, and adoption of our proposed RE-based RSU for smart and connected street infrastructure as part of the Smart Transportation Innovation Program (STIP) in partnership with the cities of Chula Vista, Carlsbad and San Diego http://globalstip.ucsd.edu/.
Last Modified: 01/26/2021
Modified by: Sujit Dey
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