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020 ▼a 9780438154421
035 ▼a (MiAaPQ)AAI10791983
035 ▼a (MiAaPQ)purdue:22521
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Li, Yimei.
24510 ▼a Data Compression in Multi-hop Large-scale Wireless Sensor Networks.
260 ▼a [S.l.] : ▼b Purdue University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 125 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Yao Liang
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity
520 ▼a For some WSN scenarios, CS may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significan
590 ▼a School code: 0183.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Purdue University. ▼b Computer Sciences.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997675 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자