Efficient Retrieval of User Contents in MANETs

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Efficient Retrieval of User Contents in MANETs
Data Engineering Laboratory, Aristotle
University of Thessaloniki

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MANETs

• Nodes are free to move, join or leave the network
• Bandwidth constrained links
• Multi-hop communication
• P2P approach in most cases
• Limited computation power and cache space
• Two fundamentals problems arise
• How to discover services and resources available
  at other nodes
• How to transfer information between two network
  nodes

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Eureka

• Key idea
• Exploit the information density concept and allow
  users to estimate where in MANET the information
  they are looking for can be found
• Advantages
• Waste of bandwidth is avoided by selectively
  forwarding content queries
• Fewer replies messages
• Fewer collisions
• The use of GPS is not required
• Applicable for VANETs
• The road topology reduces the regions where
  information can be found
• Highly mobile environment

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System and Assumptions (1/2)

• One or more gateways
• Each node is equipped with a data cache
• Pure P2P system
• N data items. Each item is divided into units,
  called chunks
• The missing chunks can be retrieved from
  different nodes
• Each node requests a data item i with rate ?i.
  Each node knows the number of chunks into which a
  data item is divided
• A node responds with information message
• A node rebroadcasting a request stores ltqueryID,
  srcAddress, prevNodegt and sets query status to
  pending

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System and Assumptions (2/2)

• All nodes listen to channel gt A pending query
  could be transformed to solved
• Each query header includes a HOP_COUNT
• Each node computes an information density
  estimate in a distributed manner for each item
• Requester node adds to the header the
  ESTIMATED_DENSITY
• Node forwards a request if its own estimate is
  higher than that carried by the request
• It is used a query lag time similar to DSR
• It is used a query time to live to shorten the
  reach of broadcast queries

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Information Density Estimation (1/4)

• The information density function, di(x, y), is
  defined as the spatial density of information
  chunks cached at nodes participating in the
  network, around a point whose spatial coordinates
  are (x, y)
• At each sampling step j, a node n computes an
  information density sample si,j(n) for each data
  item it is aware of, by using information
  captured within its reach range
• The sample si,j(n) represents the estimated
  number of new copies of chunks (during step j)
• Local information density sample sli,j(n)
• If node n generates a reply message to node Q the
  contribution that is added to sli,j(n) is
  1-(hQ-1)/TTL

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Information Density Estimation (2/4)

• If node n receives a new transiting information
  message from node I to node Q, the contribution
  is (1-(hQ-1)/TTL)(1-(hI-1)/TTL)
• The last case accounts for the reception of an
  information message whose contribution must not
  (or cannot) be related to a corresponding query
  the contribution is 1-(hI-1)/TTL

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Information Density Estimation (3/4)

• Distributed information density sample sdi,j(n)
• Every time a node m generates or relays a query
  for some chunks of data item i, it advertises its
  local information density sample for this item,
  sli,j(m)
• A node n receiving the query computes ?m?Mi,j
  (n) sli,j(n)/Mi,j(n)
• Overall information density sample
• For each step j (sli,j(n) sdi,j(n)) / 2

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Information Density Estimation (4/4)

• Filtering and information density estimate
• The filter is built so that the value of each new
  sample is kept almost constant to its original
  value for W sampling steps since it was computed,
  after which it is exponentially decreased
• The average cache time of chunks at nodes is 1/µ
• The sampling frequency is given by fc 1/ Tc
  Wµ
• The larger the W, the higher the sampling
  frequency