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Resource-Constrained Intelligent Edge Systems for Internet-of-Things Applications
Resource-Constrained Intelligent Edge Systems for Internet-of-Things Applications
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103645
- ISBN
- 9798314874776
- DDC
- 621.3
- 서명/저자
- Resource-Constrained Intelligent Edge Systems for Internet-of-Things Applications
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 116 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Blaauw, David;Kim, Hun-Seok.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Millimeter-scale Internet of Things (IoT) systems can be uniquely valuable in applications like wearables and environmental monitoring by obtaining and analyzing data directly at the source without being disruptive. By incorporating edge intelligence, miniaturized IoT systems can achieve greater autonomy, transforming application spaces. These devices are powered by small-scale batteries, resulting in a limited energy budget that constrains computational complexity, memory capacity, and wireless communication. This dissertation introduces techniques at the algorithmic, hardware, and system level that tackle these challenges, and demonstrates three miniature IoT systems.The first work is a 6.7 x 7 x 5 mm ultra-low-power imaging system with deep learning and image processing capabilities for intelligent edge monitoring. It leverages data and energy management techniques, including hierarchical event detection, dynamic power management, and image data compression methods, to maintain a low average power less than 50 µW. Image correction layers are introduced to improve deep neural network (DNN) performance in constrained embedded systems. The second work presents an orthogonal frequency-division multiple access (OFDMA) baseband localization processor fabricated in 22 nm, co-designed with a low-power crystal-less radio frequency (RF) receiver, that efficiently estimates the channel frequency response in real-time for IoT localization. This system is deployed in a 1 x 2.8 cm tag that achieves 4.3x longer distance and 6.6x lower power than the state-of-the-art. The final work demonstrates an 8.5 x 20 mm tag that integrates the proposed baseband localization processor and low-power RF receiver with custom integrated circuits (ICs) and a narrowband antenna to enable insect tracking. The system consumes an average of 27 µW for 2D narrowband localization, stores a maximum 372-point trajectory, and achieves sub-meter accuracy in flat-fading environments.The techniques presented in this dissertation showcase advancements in overcoming the limitations of millimeter-scale IoT systems. The demonstrated systems highlight the potential of cross-level optimization between system resource management, application-specific hardware, and efficient algorithms to enable miniature intelligent devices.
- 일반주제명
- Computer engineering
- 일반주제명
- Electrical engineering
- 기타저자
- University of Michigan Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798314874776
■035 ▼a(MiAaPQ)AAI32092614
■035 ▼a(MiAaPQ)umichrackham005996
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aBejarano, Andrea Maria.
■24510▼aResource-Constrained Intelligent Edge Systems for Internet-of-Things Applications
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a116 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Blaauw, David;Kim, Hun-Seok.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aMillimeter-scale Internet of Things (IoT) systems can be uniquely valuable in applications like wearables and environmental monitoring by obtaining and analyzing data directly at the source without being disruptive. By incorporating edge intelligence, miniaturized IoT systems can achieve greater autonomy, transforming application spaces. These devices are powered by small-scale batteries, resulting in a limited energy budget that constrains computational complexity, memory capacity, and wireless communication. This dissertation introduces techniques at the algorithmic, hardware, and system level that tackle these challenges, and demonstrates three miniature IoT systems.The first work is a 6.7 x 7 x 5 mm ultra-low-power imaging system with deep learning and image processing capabilities for intelligent edge monitoring. It leverages data and energy management techniques, including hierarchical event detection, dynamic power management, and image data compression methods, to maintain a low average power less than 50 µW. Image correction layers are introduced to improve deep neural network (DNN) performance in constrained embedded systems. The second work presents an orthogonal frequency-division multiple access (OFDMA) baseband localization processor fabricated in 22 nm, co-designed with a low-power crystal-less radio frequency (RF) receiver, that efficiently estimates the channel frequency response in real-time for IoT localization. This system is deployed in a 1 x 2.8 cm tag that achieves 4.3x longer distance and 6.6x lower power than the state-of-the-art. The final work demonstrates an 8.5 x 20 mm tag that integrates the proposed baseband localization processor and low-power RF receiver with custom integrated circuits (ICs) and a narrowband antenna to enable insect tracking. The system consumes an average of 27 µW for 2D narrowband localization, stores a maximum 372-point trajectory, and achieves sub-meter accuracy in flat-fading environments.The techniques presented in this dissertation showcase advancements in overcoming the limitations of millimeter-scale IoT systems. The demonstrated systems highlight the potential of cross-level optimization between system resource management, application-specific hardware, and efficient algorithms to enable miniature intelligent devices.
■590 ▼aSchool code: 0127.
■650 4▼aComputer engineering
■650 4▼aElectrical engineering
■653 ▼aInternet of Things
■653 ▼aOrthogonal frequency-division multiple access
■690 ▼a0544
■690 ▼a0464
■71020▼aUniversity of Michigan▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358109▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
