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Data-Driven Optimization for Low-Power Wide-Area Network Planning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Optimization for Low-Power Wide-Area Network Planning.
作者:	Aarts, Sander.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	169 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
附註:	Advisor: Shmoys, David.
Contained By:	Dissertations Abstracts International85-03B.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631424
ISBN:	9798380315623

Data-Driven Optimization for Low-Power Wide-Area Network Planning.
Aarts, Sander.

Data-Driven Optimization for Low-Power Wide-Area Network Planning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 169 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--Cornell University, 2023.

This item must not be sold to any third party vendors.

Low-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.

ISBN: 9798380315623Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Algorithms

Data-Driven Optimization for Low-Power Wide-Area Network Planning.
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520 $a Low-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.
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