Location Directory Services

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Location Directory Services

• Vivek Sharma
• 9/26/2001

CS851 Large Scale Deeply Embedded
Networks
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Overview

• Problem Statement
• Related work
• Design Issues
• Papers we shall discuss today
• Grids Location Service (GLS)
• Randomized Database Groups (RDG)
• Comparison and Issues
• Conclusion

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Problem Statement

• A directory service for a sensor network where
  nodes can lookup the geographical location of
  other nodes. The service implementation should be
• Distributed among the nodes
• Resilient to node failures
• Scalable to a large number of nodes
• Should have low memory and communication/power
  overheads

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Related Work

• Location Management in Mobile Systems
• tracking mobility of users to route calls
  efficiently
• the network has fixed nodes with much more
  resources
• most of the architectures are hierarchical and
  thus not fault tolerant
• Ad Hoc Networks
• conditions closest to a typical sensor network
  (no fixed infrastructure)
• additional power, communication and scalability
  issues apply
• Smart Spaces
• locating people and equipment in an office like
  environment
• relative to a fixed set of wireless receivers

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Related Work

• Peer-to-Peer Applications
• a distributed service to locate nodes with
  particular data items
• no resource limitations or mobility in the system
• Resource Location Problems
• spatial gossip algorithms

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Design Issues
Proactive vs.
Reactive (maintaining location
(on demand determination) informa
tion continuously)
Deterministic vs.
Non-Deterministic (e.g., hashing or ID mapping)
(randomized approaches in
choosing location servers)
Hierarchical vs.
Flat distributed set of arrangement of
location servers location
servers
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Deterministic vs. Non-deterministic approaches

• Non-deterministic approaches as opposed to
  deterministic approaches are usually inherently
  resilient and are capable of handling large
  degrees of node failure and mobility
• The main problem while using a random approach is
  to control the randomization to provide desired
  behavior and to reduce the overheads of a random
  approach
• In deterministic approaches, one has to
  especially work towards providing
  fault-tolerance. Generally, its extra work to
  ensure that a system is resilient to failures

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Papers to be covered

• Grids Location Service (GLS)
• A scalable location service for geographic ad hoc
  routing Jannotti et al (MIT)
• a location service based on selecting location
  servers based on node ID hash values
• Randomized Database Groups (RDG)
• Ad-hoc mobility management with Randomized
  Database Groups - Haas and Liang (Cornell)
• a non-deterministic approach towards maintaining
  location information

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Grids Location Service (GLS)

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GLS Overview

• The location service is used to enable
  geographical
• ad-hoc routing
• The network is divided into ordered grids or
  squares and each node is aware of the divisions
• Each node determines its geographic position
  using a mechanism such as GPS
• Every node maintains a table of its current
  neighbors identities and locations (each node
  broadcasts periodic HELLO packets)

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GLS Overview

• Location Servers Every node selects a group of
  nodes (location servers for that node)
  distributed throughout the network, where it
  maintains its current location.
• Routing the location of the destination is
  determined by performing a location query and
  routing is then done using Geographic Forwarding.
• Geographic Forwarding When a node needs to send
  a packet towards location P, the node forwards
  the packet to the node amongst its neighbors
  which is closest to P.

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Example
Bs location servers
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Selecting and Querying Location Servers

• Selection A node recruits other nodes with IDs
  close to its own ID as its location servers.
  Location servers are selected in each sibling of
  a square that contains the node.
• Querying A sends a request to the least node
  greater than B for which it has information. That
  node forwards the query in the same way.
  Eventually the query will reach a location server
  of B which will forward the query to B itself. B
  can now respond directly.

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Querying Location Servers Example
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Updating Location Information

• A node updates its order-2 location servers every
  time it moves a threshold distance d, its order-3
  servers when it moves a threshold distance 2d,
  and so on. So, a node sends out updates
  proportional to its speed and updates are sent to
  distant servers less often than to local servers
• Forwarding Pointers are used at the order 1 grid
  to let farther nodes route correctly when a node
  moves out of its square

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Simulation Scenario

• Monarch CMUs wireless extensions for ns.
• 802.11 Radio
• Bandwidth1Mbps
• Radio range 250m.
• 100 nodes/km2
• Order-1 square side 250 m
• Mobility random waypoint model
• Network of 600 nodes the scale of a campus or
  city

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Results

• Scalability of GLS

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Results

• Performance of GLS in
• the presence of mobility

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Results

• Performance of GLS
• with node failures

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Pros and Cons

• Pros
• Each node has to maintain a small amount of state
• The querying technique is not paralyzed by
  failure of location servers
• Cons
• Prone to performance degradation due to node
  failures and high degrees of mobility
• Fixed size squares nodes in high density areas
  have to maintain more state information so there
  is much more stress on these nodes in terms of
  power
• The nodes should know the GRID structure
  beforehand

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Randomized Database Groups (RDG)

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RDG Overview

• A set of location databases form a virtual
  backbone, which is dynamically formed and
  distributed among the nodes.
• Location update a node writes its location to a
  randomly chosen group of k databases
• Location lookup A randomly chosen group of k
  databases is queried.
• The destination node location is provided to the
  source by the databases at the intersection of
  the queried database group and the group last
  written to by the destination node.

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The virtual backbone

• Formation During initial setup, network flooding
  could be used to find the set of nodes that best
  serve as the backbone (e.g. uniformly
  distributed)
• Maintenance When a backbone node is detached
  from the network, a nearby non-backbone node is
  recruited to take its place

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Randomized Database Groups

• Given a virtual backbone with n location
  databases, any combination of k databases forms a
  RDG
• When a node needs to update its location
  information, it uses any accessible RDG out of
  the nCk possible. Same for location query
• k could be different for different nodes
  depending on the nodes traffic and mobility
  patterns
• With appropriately chosen k, the probability of
  non-intersection between the set of databases
  queried and the set of databases updates can be
  made sufficiently small

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Example
n 6 databases e.g. of RDG all combinations of
size k3 1,2,3,1,2,4,1,2,5,. A
node accesses the set of databases through the
database nearest to it.
Virtual Backbone and the Location Databases
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Mobile Location Updates

• Call-origination update the querying node writes
  its current location into the queried databases.
• Location-change update When a node changes
  location, it updates its new location in a RDG.
• Periodic Update Apart from the above, a node
  sends location information at every interval.

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Mobility Management Costs

• pe probability that a database is inaccessible
  at any time instant.
• fo(t) PDF for the length of time between any
  two consecutive call originations
• fm(t) PDF for the length of time between any
  two consecutive location change updates.
• Tp Periodic update interval
• cu expected cost of accessing a database
• cl expected miss penalty.
• Cupdate k cu
• Closs cl X Expected number of lost calls per
  unit time

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Optimal RDG size determination

• We can see that even for high pe, optimal cost is
  achieved with low k due to the tradeoff in the
  cost metric

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Pros and Cons

• Pros
• Allows tuning of performance based on expected
  parameter values for the system
• Expected to handle large degrees of node failures
  well
• Can be made adaptive to each nodes traffic and
  mobility patterns
• Cons
• Communication overheads could be significant with
  respect to other approaches due to maintaining
  redundant location info
• Greater load on the location databases so life
  time could be low for those nodes (although these
  nodes need not be on all the time)
• Analytical results, a lot of assumptions.
  Unfortunately no simulations to get an idea of
  performance in scenarios

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Comparison

• RDG
• Non-deterministic selection of location databases
• Scalability
• k is likely to be high implying storing more
  state information
• Location servers are especially marked out and
  hence greater load on them (power)
• Inherently fault resilient due to the random
  approach
• Expected to handle high degrees of node mobility
  and node switch-offs better (though maybe at a
  higher cost?)

• GLS
• Deterministic ID based technique to select
  location servers
• Scalability
• State maintenance overheads are low
• Location information is spread out on all nodes
  (Asm density)
• Reasonably resilient to node failures due to less
  state info and robust querying method
• Performance degradation in the presence of a high
  degree of mobility and node switch-offs could be
  significant

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Conclusions

• A randomized approach is attractive because of
  its inherent capacity to handle high degrees of
  mobility and provide high degrees of resilience
• But some of these advantages could be offset by
  the amount of overheads due to redundancy in the
  state information maintained
• The GLS technique uses techniques similar to
  hashing to distribute location information evenly
  on the set of nodes and uses intelligent
  heuristics to provide a robust location querying
  service

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Some Issues

• The implementation of a location directory
  service could impose significant overheads on the
  system
• Questions to ask -
• Do we really need a location directory service?
• ID-less routing, Directed Diffusion
• Is the value added more than the costs?
• It might not sound feasible or necessary to have
  a global location service for sensor networks.
  One could consider having
• a higher level directory service to map Data or
  Tasks to locations, and
• a lower level directory service to map node-IDs
  to locations within groups

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Thanks!!

• Vivek Sharma