Brian D. Noble

Computer Networks and Communications · 2001–2008

A researcher in the Science corpus: 4 papers; trusts 8 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

Whom Brian D. Noble trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Ben Liang
  2. Ranveer Chandra
  3. Elizabeth M. Belding-Royer
  4. Anthony LaMarca
  5. Zygmunt J. Haas
  6. David F. Kotz

Who trusts Brian D. Noble overall

  1. Aruna Devi Balasubramanian
  2. Jeongyeup Paek
  3. Tal Garfinkel
  4. Kyunghan Lee
  5. Mihail L. Sichitiu
  6. Arvind Thiagarajan

Papers from Brian D. Noble's perspective

Ordered by how strongly the researchers Brian D. Noble trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Accuracy characterization for metropolitan-scale Wi-Fi localization (ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services)
  2. Performance comparison of two on-demand routing protocols for ad hoc networks (Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064))
  3. Practical robust localization over large-scale 802.11 wireless networks
  4. Place Lab: Device Positioning Using Radio Beacons in the Wild (International Conference on Pervasive Computing)
  5. An analysis of the optimum node density for ad hoc mobile networks (ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240))
  6. Bayesian Filtering for Location Estimation (IEEE pervasive computing)
  7. Random waypoint considered harmful (IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428))
  8. Evaluating location predictors with extensive Wi-Fi mobility data (IEEE INFOCOM 2004)
  9. Characterizing residential broadband networks (ACM/SIGCOMM Internet Measurement Conference)
  10. A Tutorial on Support Vector Machines for Pattern Recognition

Their papers