research-article

An empirical study of bandwidth predictability in mobile computing

Authors:

Salil S. Kanhere,

Mahbub HassanAuthors Info & Claims

WiNTECH '08: Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization

Pages 11 - 18

https://doi.org/10.1145/1410077.1410081

Published: 19 September 2008 Publication History

Abstract

While bandwidth predictability has been well studied in static environments, it remains largely unexplored in the context of mobile computing. To gain a deeper understanding of this important issue in the mobile environment, we conducted an eight-month measurement study consisting of 71 repeated trips along a 23Km route in Sydney under typical driving conditions. To account for the network diversity, we measure bandwidth from two independent cellular providers implementing the popular High-Speed Downlink Packet Access (HSDPA) technology in two different peak access rates (1.8 and 3.6Mbps). Interestingly, we observe no significant correlation between the bandwidth signals at different points in time within a given trip. This observation eventually leads to the revelation that the popular time series models, e.g. the Autoregressive and Moving Average, typically used to predict network traffic in static environments are not as effective in capturing the regularity in mobile bandwidth. Although the bandwidth signal in a given trip appears as a random white noise, we are able to detect the existence of patterns by analyzing the distribution of the bandwidth observed during the repeated trips. We quantify the bandwidth predictability reflected by these patterns using tools from information theory, entropy in particular. The entropy analysis reveals that the bandwidth uncertainty may reduce by as much as 46% when observations from past trips are accounted for. We further demonstrate that the bandwidth in mobile computing appears more predictable when location is used as a context. All these observations are consistent across multiple independent providers offering different data transfer rates using possibly different networking hardware.

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Cited By

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https://dl.acm.org/doi/10.1007/s11277-024-11132-0
Zhang LZhong YLiu JCui L(2023)Resource and Bandwidth-Aware Video Analytics with Adaptive Offloading2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00021(107-115)Online publication date: 25-Sep-2023
https://doi.org/10.1109/MASS58611.2023.00021
Bose SMukherjee N(2022)SenSchedule: Scheduling Heterogeneous Resources in Sensor-Cloud InfrastructureIEEE Transactions on Services Computing10.1109/TSC.2020.302267915:4(1825-1840)Online publication date: 1-Jul-2022
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Index Terms

An empirical study of bandwidth predictability in mobile computing
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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cover image ACM Conferences

WiNTECH '08: Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization

September 2008

122 pages

ISBN:9781605581873

DOI:10.1145/1410077

General Chair:
Ashutosh Sabharwal
Rice University, USA
,
Program Chairs:
Roger Karrer
Deutsche Telekom Laboratories/TU Berlin, Germany
,
Lin Zhong
Rice University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 September 2008

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MobiCom08

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MobiCom08: Annual International Conference on Mobile Computing and Networking

September 19, 2008

California, San Francisco, USA

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Overall Acceptance Rate 63 of 100 submissions, 63%

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https://dl.acm.org/doi/10.1007/s11277-024-11132-0
Zhang LZhong YLiu JCui L(2023)Resource and Bandwidth-Aware Video Analytics with Adaptive Offloading2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00021(107-115)Online publication date: 25-Sep-2023
https://doi.org/10.1109/MASS58611.2023.00021
Bose SMukherjee N(2022)SenSchedule: Scheduling Heterogeneous Resources in Sensor-Cloud InfrastructureIEEE Transactions on Services Computing10.1109/TSC.2020.302267915:4(1825-1840)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSC.2020.3022679
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