| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000431970 |
| 005 | | 20200224111656 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085654593 |
| 035 | |
▼a (MiAaPQ)AAI13897074 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Zhu, Qiuxi. |
| 245 | 10 |
▼a Exploiting Mobile Plus in-situ Deployments in Community IoT Systems. |
| 260 | |
▼a [S.l.]:
▼b University of California, Irvine.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 178 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Venkatasubramanian, Nalini. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Improvements in Internet connectivity and advances in smart personal devices have enabled the rise of the Internet of Things (IoT) in real-world communities.Community IoT deployments utilize low-cost devices, often deployed in-situ in a relatively stable environment, to create real-time situation awareness. Our experience in operating and maintaining prototype IoT systems in real-world testbeds indicates that integrating mobile devices with in-situ platforms is a promising approach to increase the reliability and sustainability of commonplace community IoT applications. In particular, mobile devices can be leveraged to compensate for the non-uniform availability of infrastructure efficiently. Realizing the potential of the combined "mobile and in-situ'' deployments requires us to address a new set of challenges for data collection in dynamic settings.In this thesis, we propose planning-based approaches to the efficient operation and maintenance of community-scale IoT deployments that consist of both mobile and in-situ devices.Our proposed techniques leverage the prior knowledge of data characteristics, device heterogeneity, community infrastructure, and application needs.The goal is to optimize the activities of the devices under data budgets and timeliness constraints and seek a balance between data utility (i.e., accuracy, importance, and timeliness) and operational cost.We explore our solution within the context of urban environmental sensing and address three major research problems regarding IoT data generation, data upload, and sensor calibration (i.e., maintenance), respectively. First, we propose a spatiotemporal scheduling framework that regulates the data generation activities of participating devices. The framework employs online planning algorithms that optimize the spatiotemporal coverage of collected data to meet the application requirements of heterogeneous data types.Second, in the case of non-uniform network availability, we design a two-phase upload planning approach that creates data upload plans (i.e., when, where, and what to upload) for mobile data collectors before their departure (i.e., the static planning phase) |
| 590 | |
▼a School code: 0030. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Electrical engineering. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0544 |
| 710 | 20 |
▼a University of California, Irvine.
▼b Computer Science - Ph.D.. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0030 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491785
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |