Routing in Mobile Ad Hoc Networks

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• Routing in Mobile Ad Hoc Networks
• using
• Collective Intelligence based Strategies

VIVEK KUMAR SINGH Department of Computer
Science, Banaras Hindu University,
Varanasi-221005 Email vivekks12_at_yahoo.co.in
PROF ASHOK K. GUPTA J. K. Institute of Applied
Physics Technology, University of Allahabad,
Allahabad-211002
NCAICT 2008
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Mobile Ad hoc Networks (MANETs)
Consists of mobile nodes which communicate with
each other through wireless medium without any
fixed infrastructure
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Mobile Ad hoc Networks (MANETs)

• Nodes are mobile and they communicate with each
  other via wireless connections i.e., Dynamic
  Topology
• Limited Processing Storing Capabilities of the
  Devices. (WSN)
• Bandwidth Constraints
• No centralized control, i.e., all nodes can serve
  as routers.

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MANET Applications

• Personal area networking
• Cell phone, laptop, ear phone
• Emergency operations
• Search and rescue
• Policing and fire fighting
• Civilian environments
• Taxi cab network
• Meeting rooms
• Sports stadiums
• Boats, aircrafts
• Military use
• On the battle field

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Problems / Challenges for MANETs

• Without a central infrastructure, things become
  much more difficult
• Problems are due to
• Lack of central entity for organization available
• Limited range of wireless communication
• Mobility of participants
• Battery-operated entities

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Routing Protocol for Ad Hoc Networks
Characteristics and tradeoffs

• Characteristics
• Decentralized
• Self-organized
• Self-deployed
• Dynamic network topology
• Tradeoffs
• ?? Bandwidth limited
• ?? Multi-hop router needed
• ?? Energy consumption problem
• ?? Security problem
• Why traditional routing protocols are not
  suitable for
• MANET networks ?

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MANET
X
X
X
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Routing Protocol for Ad Hoc Networks

• Proactive
• (table-driven)

Reactive (on-demand)
Hybrid

• DSDV
• WRP
• CGSR

• DSR
• AODV
• TORA

• ZRP

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Table Driven Routing Algorithms
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Demand Driven Routing Algorithms
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Collective Intelligence

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Collective Intelligence

• Collective Intelligence is a form of Intelligence
  that emerges from the collaboration/ coordination
  of many individuals/ entities.
• Collective intelligence appears in a wide variety
  of forms of consensus decision making in
  bacteria, animals, humans, and computers.
• MIT, USA studying the question How can people
  and computers be connected so thatcollectivelyth
  ey act more intelligently than any individuals,
  groups, or computers have ever done before?'

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Collective Intelligence (2)

• Google uses the knowledge millions of people have
  stored in the World Wide Web to provide
  remarkably useful answers to users' questions.
• Wikipedia motivates thousands of volunteers
  around the world to create the world's largest
  encyclopedia.
• Innocentive lets companies easily tap the talents
  of the global scientific community for innovative
  solutions to tough RD problems.

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Collective Intelligence (3)
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Collective Intelligence (4)

• Cooperative and Coordinated effort can be used to
  solve computationally hard problems.
• Examples are Particle Swarms, Bee Colonies, Ant
  Colony Optimization etc.

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Collective Intelligence - Advantages

• Scalability
• Fault tolerance
• Adaptation
• Speed
• Modularity
• Autonomy
• Parallelism

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Ant Colony Optimization (1)

• Ant Colony Optimization (ACO) studies artificial
  systems that take inspiration from the behavior
  of real ant colonies and which are used to solve
  discrete optimization problems.
• In 1999, the Ant Colony Optimization
  metaheuristic was defined by Dorigo, Di Caro and
  Gambardella.
• In ACO, a set of software agents called
  artificial ants search for good solutions to a
  given optimization problem.

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Ant Colony Optimization (2)

• To apply ACO, the optimization problem is
  transformed into the problem of finding the best
  path on a weighted graph.
• The artificial ants incrementally build solutions
  by moving on the graph.
• ACO has been applied to a number of areas ranging
  from combinatorial optimization problems to
  telecommunication networks.

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Ant Colony Optimization (3)
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Ant Colony Optimization (4)
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Ant Colony Optimization (5)

• During the route finding process, ants deposit
  pheromone on the edges. the amount of pheromone
  of the edge (vi, vj) on movement from node
  vi to node vj is changed as follows
• Like real pheromone the artificial pheromone
  concentration decreases with time. Typically
  given as

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Key Components of ACO
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Collective Intelligence Based Routing Algorithm
for MANETs

• Many routing algorithms based on the idea of
  collective intelligence, particularly ACO, have
  been proposed.
• Ant Net ( for wired Nets)
• Ant Based Control (ABC)
• Ant Routing Algorithm (ARA)

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Ant Net

• Routing is determined by means of very complex
  interactions of forward and backward network
  exploration agents (ants).
• The backward ants utilize the useful information
  gathered by the forward ants on their trip from
  source to destination .

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Ant Routing Algorithm (ARA)

• The algorithm has three phases
• Route Discovery Phase
• Route Maintenance
• Route Failure Handling

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Route Discovery Phase (Forward Ant)
Ant Routing Algorithm (2)
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Ant Routing Algorithm (3)
Route Discovery Phase (Backward Ant)

• Route discovery phase in ARA. a) A forward
  ant (F) is sent from the sender (vS) to the
  destination node (vD). The forward ant passes
  through by other nodes which initialize their
  routing table and the pheromone values. b) The
  backward ant (B) has the same task as the forward
  ant. It is sent by the destination node (vD) to
  the source node vS.

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Ant Routing Algorithm (4)

• A FANT is an agent which establishes the
  pheromone track back to the source node.
• a BANT establishes the pheromone track back to
  its origin, namely the destination node.
• A node which receives a FANT for the first time
  creates a record in its routing table.
• An entry in the routing table is a triple
  (destination address, next hop, pheromone value).
  

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Ant Routing Algorithm (5)

• Before updating

After updating
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Ant Routing Algorithm (6)

• Duplicate F-ANTs are identified through the
  unique sequence number, and are removed. The
  destination node extracts the information of the
  F-ANT, creates a B-ANT and returns it to the
  source node.
• The B-ANT's task is similar to that of the F-ANT,
  i.e., to establish a track to this node. When the
  sender receives the B-ANT from the destination
  node, the path is established and data packets
  can be sent.

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Route Maintenance
Ant Routing Algorithm (7)

• Established paths do not keep their initial
  pheromone values forever.
• When a node vi relays a data packet to
  destination vd through a neighbor node vj , it
  increases the pheromone value of the entry (vd,
  vj , ) by i.e. this path to the
  destination is strengthened by the data packet.
• Likewise, the next hop vj increases the pheromone
  value of the entry (vs, vi, ) by i.e.
  the backward path to the source node is also
  strengthened.

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Route Failure Handling
Ant Routing Algorithm (8)

• If a node receives a ROUTE_ERROR message for a
  certain link, it deactivates this link by setting
  the pheromone value to 0.
• the node searches for an alternative link in its
  routing table. If there is another route to the
  destination it will send the packet via this
  path.
• Otherwise, the node informs its neighbors, hoping
  that they can forward the packet to the
  destination.

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Ant Routing Algorithm (9)
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Ant Routing Algorithm (10)
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Ant Routing Algorithm (11)
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Conclusion

• Collective Intelligence based approach is
  suitable to dynamic, self organizing problems
  such routing control.
• Collective Intelligence Based strategies have
  been applied to Network Routing problem with a
  reasonable degree of goodness.
• Many strategies in simulated settings outperform
  almost all traditional approaches on a number of
  QOS parameters.

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