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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

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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


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Outline

  • The Infostation Model

  • The Shared Wireless Infostation Model (SWIM)

  • Biological Information Acquisition System

  • Network and Analytical Models

  • Simulation Results

  • Conclusions


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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


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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.


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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


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    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



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    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


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    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


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    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


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    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 =



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    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





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    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





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    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




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    Conclusions

    • 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


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