Disconnection Prediction in Mobile Ad hoc Networks for Supporting Cooperative work

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Disconnection Prediction in Mobile Ad hoc
Networks for Supporting Cooperative work

• F. D. Rosa, A. Malizia, and M. Mecella
• Presented by Ko Euiyul

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Contents

• Introduction
• Disconnection Prediction
• Experimental Results
• Pervasive Architecture for supporting cooperation
• Future Work

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Introduction

• The widespread availability of network-enabled
  handheld devices has made the development of
  pervasive computing environments an emerging
  reality.
• Computer-supported-cooperative-work tools and
  workflow management applications
• Typically require continuous connections
• Pervasive architecture that can maintain
  continuous connections among MANET devices
• How do you predict possible disconnections of
  devices?

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Disconnection Prediction

• Possible Scenario
• Archeological disaster recovery
• In this situation, members of the recovery team
  are connected continuously.
• Assumption
• Each device includes hardware that lets it know
  its communication distance from the surrounding
  devices that are within radio range.
• No device in the MANET has GPS hardware, because
  were interested in MANETs of low-profile
  devices, which normally arent equipped with GPS
• At start-up, all devices are connected and the
  pervasive architectures goal is to maintain
  these connections.
• A specific device in the MANET, called the
  coordinator, centrally predicts disconnections.

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Prediction Technique

• At given time ti, in which all devices are
  connected, the coordinator collects all distance
  information from the other devices.
• Each device sends the coordinator a message
  containing the distances to other devices within
  range.
• Coordinator builds a probable next connection
  graph, and Using the graph, the coordination
  layer enacts appropriate actions in the interval
  ti,ti1.
• Using the current situation and situations and
  predictions in the recent past for prediction

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Prediction Technique

• Reasonable assumption
• If two devices tend to go out of range if not
  controlled but are connected through the
  coordinators remedial actions, this influences
  the next probability of going out of range.
• Consider a time frame of h gt 0 time units as the
  history of distances between devices i,j.
• Predicted distance between i and j at the next
  time unit as

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Prediction Technique

• The estimated probability of devices (i,j) still
  being in range at t 1 is

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The MGR Algorithm

• The Mobile Gamblers Ruin (MGR) algorithm

• M E?E
• E m, the number of MANET mobile devices.
• M is an m?m symmetric matrix
• diagonal elements

• The MGR algorithm first finds the graphs
  connected components and verifies if two devices
  belong to the same connected component.

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The MGR Algorithm

• Example
• Dealing with error
• The error ?S is typically small compared to .
  So this error only partially affects the
  prediction models average error.

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Experimental Results

• Obstacle Mobility Model
• Provides a mechanism for modeling movement in
  real-world environments.
• Simulation area
• 1000? ? 1000?, where ? (1/100)Sdev
• The mobility of nodes is randomly selected
  between 0 and 5 m/s to represent walking speeds.
• At creating, nodes are randomly distributed but
  loosely connected.
• Random obstacle
• Simulator
• NS and GloMoSim
• 5-device set and 10-device set, with 7 randomly
  placed obstacles
• 2000s time frame and performed 50 experiments
  obtaining between 50,000 and 100,000 samples per
  experiment.
• Time frame h of 5, 10, 15.

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Experimental Results

• Worst case
• 2.5? (means that MGR has a precision bound of
  97.5 in predicting distances between mobile
  devices)
• Best case
• 0.5? (measn that MGR has a precision bound of
  99.5)
• In this case, coordinator must devote more space
  to maintain all the previous S.

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Pervasive Architecture for supporting cooperation
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Pervasive Architecture for supporting cooperation

• Wireless Stack
• A wireless network interface and the hardware for
  calculating distances from neighbors
• Network Service Interface
• Offers to upper layers the basic services for
  sending and receiving message
• Predictive layer
• Prediction of disconnection
• Coordination layer
• Manage situations when a peer is going to
  disconnect

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Pervasive Architecture for supporting cooperation

• Local connection management
• Monitoring and checking one-hop communications
  between a device and its neighbors
• Global management
• Consistent state of the network and of each peer
  in the network
• Manage network topology and the tasks each peer

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Future Work

• Complete the coordination layer and validate
  approach in real scenarios.
• Consider sudden downs of devices
• Convert central approach to distributed

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