slide1 l.
Skip this Video
Download Presentation

Loading in 2 Seconds...

play fullscreen
1 / 24

- PowerPoint PPT Presentation

  • Uploaded on

The Shared Wireless Infostation Model – A New Ad Hoc Networking Paradigm (or Where there is a Whale, there is a Way). Zygmunt J. Haas School of Electrical and Computer Engineering Cornell University Ithaca, NY 14853, USA. Tara Small Field of Applied Mathematics Cornell University

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about '' - arleen

Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

The Shared Wireless Infostation Model – A New Ad Hoc Networking Paradigm(or Where there is a Whale, there is a Way)

Zygmunt J. Haas

School of Electrical and Computer Engineering

Cornell University

Ithaca, NY 14853, USA

Tara Small

Field of Applied Mathematics

Cornell University

Ithaca, NY 14853, USA

ACM MobiHoc’03, June 1-3, 2003, Annapolis, Maryland

Presented by: Ahmed Sobeih



  • The Infostation Model
  • The Shared Wireless Infostation Model (SWIM)
  • Biological Information Acquisition System
  • Network and Analytical Models
  • Simulation Results
  • Conclusions

Why Infostations ?

  • Cellular wireless systems
    • Were built to carry voice traffic for people accustomed to the reliability and ubiquity of fixed telephone service.
    • Goals were :
        • high coverage (anytime anywhere)
        • low delay (voice communications requirement)
    • However, this comes at the expense of limited capacity
    • Hence, cellular wireless systems are not suitable for data traffic because of limited capacity and high cost
  • The goal of the Infostation model is to provide low-cost high-capacity wireless data communication

What are Infostations ?

  • Developed at WINLAB (Rutgers University)
  • Infostations are base stations that provide strong radio signal quality to small disjoint geographical areas and, hence, offer very high rates to users in these areas.

Pros and Cons of Infostations

  • (+) Very High Bit-Rates (1 Mbps to 1 Gbps)
  • (+) Simple and Inexpensive
  • (-) Intermittent Connectivity:
      • A node that wishes to transmit data must be located inside the Infostations’ coverage areas and must always transmit to an Infostation directly
  • (-) Significant Delays:
      • A node must wait until it becomes inside the coverage area of an infostation
  • Hence, infostations are mainly suitable to non-delay critical applications (i.e., applications which can tolerate significant delays) such as certain types of data acquisition systems

Shared Wireless Infostation Model (SWIM)

  • Goal:
    • Reduce the significant delays experienced in the Infostation model
  • Basic Idea:
    • Information reaches the Infostation by replicating and diffusing itself in the network using mobile nodes as physical carriers
  • As its name implies, SWIM is an integration of
    • the Infostations concept with the ad hoc networking model

Pros and Cons of SWIM

  • (+) Reduced Delays:
      • Allowing the packet to spread throughout the mobile nodes, the delay until one of the replicas reaches an Infostation can be significantly reduced
  • (-) Less network capacity (capacity-delay tradeoff):
      • Spreading of the packets to other nodes consumes network capacity
  • (-) Increase in storage requirements
  • Contributions of the paper:
    • Study the SWIM concept through an example application: biological information acquisition system
    • Control the capacity-delay tradeoff by controlling the parameters of the packet (i.e., disease) spreading

A Biological Information Acquisition System

  • Tagging: a primary method of collecting data from whales
  • Data collected in continuous manner, partitioned into discrete packets, and stored in memory with packet identifiers
  • As a whale comes in close proximity to another whale, the stored information may be transmitted and stored in the other whale’s memory as well
  • As the whales migrate throughout the system, a whale that comes in close contact with one of the SWIM stations, offloads all
  • the data in its memory (whether its own data or data from
  • other whales) onto the SWIM station at high bit-rate
  • After offloading its stored information, the whale’s memory
  • is then cleared

Network Model

  • Placing of SWIMs:
      • On buoys, floating on the water
  • Moving information from SWIMs to data centers at shore
      • Left open, could use satellite, ad hoc network of SWIMs, etc.
  • Duplicate Suppression
      • Each packet carries a distinct identifier
  • Packet Lifetime
      • TTL of a packet carries its remaining time. When a whale shares a packet with another whale, TTL is reduced by the duration that the source whale carried the packet; hence, no clock synchronization is needed
  • Storage Requirement
      • When TTL expires, packet is discarded from the tag’s memory

Analytical Model

  • Question: Given probability p, what is the necessary TTL of a packet, so one can be confident that with this probability p, a packet will be offloaded to one of the SWIMs?

I = # of infected whales (i. e., DO have packet stored in memory)

S = # of susceptible whales (i. e., do NOT have packet in memory but MAY get it)

R = # of recovered whales (i. e., do NOT have packet in memory and will NOT get it once more)

= contact rate of the whales

= whale-buoy contact rate

Total Infection Rate =

Total Recovery Rate =


Comparison of analysis and simulation

  • Using simulations, obtain F(T)
  • Using simulations, obtain β and γ
  • Using β and γ,compute theoretical F(T)
  • Use Χ2 statistical test to compare the theoretical F(T) distribution with the F(T) obtained from the simulation

Grouping/Feeding Mobility

  • All the previous results use "Random Midway Mobility Model"
  • - At the beginning of each time interval t
  • - Randomly choose velocity from [vmin, vmax]
  • - Randomly choose direction between [0, 2π]
  • More realistic grouping/feeding mobility model
  • - Direction is determined by weighted vector sum of
  • - Direction of migration
  • - Direction of the nearest female whale
  • - Direction of the nearest feeding area if the whale
  • is hungry

Multitier Mobility

  • All the previous results assume fixed SWIMs.
  • Another possible model is to consider mobile SWIMs as well as mobile nodes (i.e., whales)
      • e.g., SWIMs mounted on seabirds


  • SWIM significantly reduces end-to-end delay
  • SWIM incurs a modest increase in the storage requirement
  • Buoy positions and mobility greatly affects system reliability (probability that a packet reaches a buoy), while grouping and feeding weights have much smaller impact
  • Increasing the number of buoys increases reliability by increasing the cost of the system
  • Increasing the number of whales increases reliability without increasing the cost of the system