City-Wide Signal Strength Maps: Prediction with Random Forests-Reference-Cited by-同舟云学术

City-Wide Signal Strength Maps: Prediction with Random Forests

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Alimpertis Emmanouil¹,Markopoulou Athina¹,Butts Carter¹,Psounis Konstantinos²

Affiliation:

1. University of California, Irvine, USA

2. University of Souther California, USA

Publisher

ACM Press

Reference32 articles.

1. A. Shubaa, A. Le, E. Alimpertis, M. Gjoka, A. Markopoulou. 2016. AntMonitor: System and Applications. arXiv:1611.04268 (Nov. 2016).

2. E. Alimpertis, N. Fasarakis-Hilliard, and A. Bletsas. 2014. Community RF sensing for source localization. IEEE Wireless Comm. Letters 3, 4 (2014), 393-396.

3. E. Alimpertis and A. Markopoulou. 2017. A system for crowdsourcing passive mobile network measurements. In 14th USENIX NSDI'17, Posters Sessions. USENIX Association, Boston, Massachusetts, USA.

4. T. Bilen, B. Canberk, and K. R. Chowdhury. 2017. Handover Management in Software-Defined Ultra-Dense 5G Networks. IEEE Network 31, 4 (2017), 49-55.

5. L. Breiman. 2001. Random Forests”. Machine Learning 45, 1 (Oct. 2001), 5-32.

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