Measuring Traffic on the Wireless Medium: Experience and Pitfalls

Abstract

A number of measurement studies have examined traffic characteristics in wireless networks. Most of these measurements have been conducted from the wired portion of the network. In this paper we argue that such measurements are not sufficient to expose either the characteristics of the wireless medium or how such characteristics impact traffic patterns. While it is easier to make consistent measurements in the wired part of a network, such measurements can not observe the significant vagaries present in the wireless medium itself. As a consequence constructing an efficient and accurate measurement system from a wireless vantage point is important but usually quite difficult. In our work we have explored the various issues in implementing such a system to monitor traffic in an 802.11 based wireless network. We identify different challenges in making such measurements and provide detailed experimental evidence in their supports. Our work shows that the wireless measurement allows us to infer much richer information about the medium characteristics than is possible with a measurements made on the wired part of the network. We apply our measurement technique to study the end-to-end wireless network delay. We show that wireless monitoring can effectively identify the causes of end-to-end delays.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 17, 2002
Accession Number
ADA476336

Entities

People

  • Ashok Agrawala
  • Jihwang Yeo
  • Suman Banerjee

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Computer Science
  • Computers
  • Data Rate
  • Department Of Defense
  • Firmware
  • Information Operations
  • Maryland
  • Measurement
  • Monitoring
  • Networks
  • Observation
  • Operating Systems
  • Packet Loss
  • Sequences
  • Universities
  • Wireless Networks

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks