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Data-Driven Optimization for Low-Power Wide-Area Network Planning- [electronic resource]
Data-Driven Optimization for Low-Power Wide-Area Network Planning- [electronic resource]
상세정보
- 자료유형
- 학위논문
- Control Number
- 0016934615
- International Standard Book Number
- 9798380315623
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Aarts, Sander.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Cornell University., 2023
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- Physical Description
- 1 online resource(169 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- General Note
- Advisor: Shmoys, David.
- Dissertation Note
- Thesis (Ph.D.)--Cornell University, 2023.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약Low-Power Wide-Area Networks (LPWANs) are a key technology for connecting Things to the Internet. The LoRaWAN protocol is a particularly popular example, featuring over 300 million connected devices, 5.9 million wireless receivers installed, and nearly 200 public network operators. We consider the design and operation of these networks through the lens of operations research, employing modeling tools, optimization methods, and the mindset of data-driven decision-making, to develop a toolkit for planning and operating LPWANS in a principled approach. First, we formulate learnable models for both wireless connectivity and interference. Our work on interference features a new interpretable subset choice model with strong foundation in random utility theory. Secondly, leaning on data-derived insights, we formulate a wireless receiver placement problem as a covering integer program, which can be stylized as a set cover problem. Motivated by geometric regularities in LoRaWAN connectivity, we develop a new algorithm for geometric set cover, improving the time-complexity of the state-of-the art, while matching the best known asymptotic approximation-ratio with respect to the shallow-cell complexity. Finally, we develop a new provably optimal cost-sharing mechanism for the more general covering integer program that uses duality in a strengthened LP-formulation. We use the mechanism to better understand and guide cost-, and infrastructure-sharing between LPWANs.
- Subject Added Entry-Topical Term
- Computer science.
- Subject Added Entry-Topical Term
- Statistics.
- Index Term-Uncontrolled
- Algorithms
- Index Term-Uncontrolled
- Internet of Things
- Index Term-Uncontrolled
- Optimization
- Index Term-Uncontrolled
- Wireless networks
- Index Term-Uncontrolled
- Data-derived insights
- Added Entry-Corporate Name
- Cornell University Operations Research and Information Engineering
- Host Item Entry
- Dissertations Abstracts International. 85-03B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:643809
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aAarts, Sander.▼0(orcid)0000-0003-1852-9116
■24510▼aData-Driven Optimization for Low-Power Wide-Area Network Planning▼h[electronic resource]
■260 ▼a[S.l.]▼bCornell University. ▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a1 online resource(169 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Shmoys, David.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aLow-Power Wide-Area Networks (LPWANs) are a key technology for connecting Things to the Internet. The LoRaWAN protocol is a particularly popular example, featuring over 300 million connected devices, 5.9 million wireless receivers installed, and nearly 200 public network operators. We consider the design and operation of these networks through the lens of operations research, employing modeling tools, optimization methods, and the mindset of data-driven decision-making, to develop a toolkit for planning and operating LPWANS in a principled approach. First, we formulate learnable models for both wireless connectivity and interference. Our work on interference features a new interpretable subset choice model with strong foundation in random utility theory. Secondly, leaning on data-derived insights, we formulate a wireless receiver placement problem as a covering integer program, which can be stylized as a set cover problem. Motivated by geometric regularities in LoRaWAN connectivity, we develop a new algorithm for geometric set cover, improving the time-complexity of the state-of-the art, while matching the best known asymptotic approximation-ratio with respect to the shallow-cell complexity. Finally, we develop a new provably optimal cost-sharing mechanism for the more general covering integer program that uses duality in a strengthened LP-formulation. We use the mechanism to better understand and guide cost-, and infrastructure-sharing between LPWANs.
■590 ▼aSchool code: 0058.
■650 4▼aComputer science.
■650 4▼aStatistics.
■653 ▼aAlgorithms
■653 ▼aInternet of Things
■653 ▼aOptimization
■653 ▼aWireless networks
■653 ▼aData-derived insights
■690 ▼a0796
■690 ▼a0984
■690 ▼a0463
■71020▼aCornell University▼bOperations Research and Information Engineering.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934615▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
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