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An Adaptive Opportunistic Routing Scheme
            for Wireless Ad-hoc Networks

ABSTRACT


        A distributed adaptive opportunistic routing scheme for multi-
hop wireless ad-hoc networks is proposed. The proposed scheme
utilizes a reinforcement learning framework to opportunistically route
the packets even in the absence of reliable knowledge about channel
statistics and network model. This scheme is shown to be optimal with
respect to an expected average per packet reward criterion. The
proposed routing scheme jointly addresses the issues of learning and
routing in an opportunistic context, where the network structure is
characterized by the transmission success probabilities. In particular,
this learning framework leads to a stochastic routing scheme which
optimally “explores” and “exploits” the opportunities in the network.

Existing System
       Such fixed path schemes fail to take advantages of broadcast
nature and opportunities provided by the wireless medium and result
in unnecessary packet retransmissions. The opportunistic routing
decisions, in contrast, are made in an online manner by choosing the
next relay based on the actual transmission outcomes as well as a rank
ordering of neighboring nodes. Opportunistic routing mitigates the
impact of poor wireless links by exploiting the broadcast nature of
wireless transmissions and the path diversity.


Proposed System

      We investigate the problem of opportunistically routing packets
in a wireless multi-hop network when zero or erroneous knowledge of
transmission success probabilities and network topology is available.
Using a reinforcement learning framework, we propose an adaptive
opportunistic routing algorithm which minimizes the expected
average per packet cost for routing a packet from a source node to a
destination.
Our proposed reinforcement learning framework allows for a
low complexity, low overhead, distributed asynchronous
implementation. The most significant characteristics of the proposed
solution are:
    • It is oblivious to the initial knowledge of network.
    • It is distributed; each node makes decisions based on its belief
       using the information obtained from its neighbors.
    • It is asynchronous; at any time any subset of nodes can update
       their corresponding beliefs.

IMPLEMENTATION
      Implementation is the stage of the project when the
theoretical design is turned out into a working system. Thus it
can be considered to be the most critical stage in achieving a
successful new system and in giving the user, confidence that
the new system will work and be effective.

     The implementation stage involves careful planning,
investigation of the existing system and it’s constraints on
implementation, designing of methods to achieve changeover
and evaluation of changeover methods.

Modules:

Algorithm used d-Adaptor

Initialization stage


       We consider the problem of routing packets from a source node
o to a destination node d in a wireless ad-hoc network of d + 1 nodes
denoted by the set _ = fo; 1; 2; : : : ; dg. The time is slotted and
indexed by n _ 0 (this assumption is not technically critical and is only
assumed for ease of exposition). A packet indexed by m _ 1 is
generated at the source node o at time _m s according to an arbitrary
distribution with rate _ > 0.
Transmission Stage

       We assume a fixed transmission cost ci > 0 is incurred upon a
transmission from node i. Transmission cost ci can be considered to
model the amount of energy used for transmission, the expected time
to transmit a given packet, or the hop count when the cost is equal to
unity.



Acknowledgement Message Passing

          We discriminate amongst the termination events as
follows:We assume that upon the termination of a packet at the
destination (successful delivery of a packet to the destination) a fixed
and given positive reward R is obtained, while no reward is obtained
if the packet is terminated (dropped) before it reaches the destination.

Relay Stage

       Given a successful transmission from node i to the set of
neighbor nodes S, the next (possibly randomized) routing decision
includes 1) retransmission by node i, 2) relaying the packet by a node
j 2 S, or 3) dropping the packet all together. If node j is selected as a
relay, then it transmits the packet at the next slot, while other nodes k
6= j; k 2 S, expunge that packet. We define the termination event for
packet m to be the event that packet m is either received by the
destination or is dropped by a relay before reaching the destination.
H/W System Configuration:-

   Processor        -   Pentium –III

    Speed                    - 1.1 Ghz

    RAM                      - 256 MB(min)

    Hard Disk                - 20 GB

    Floppy Drive             - 1.44 MB

    Key Board                -    Standard Windows Keyboard

    Mouse                    - Two or Three Button Mouse

    Monitor                   -   SVGA

    Software Requirements:
            •   Operating system :- Windows XP Professional
            •   JDK              :-1.5/ 1.6 and above
            •   Front End :- JAVA, Swing(JFC),

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An adaptive opportunistic routing scheme for wireless ad hoc networks

  • 1. An Adaptive Opportunistic Routing Scheme for Wireless Ad-hoc Networks ABSTRACT A distributed adaptive opportunistic routing scheme for multi- hop wireless ad-hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme which optimally “explores” and “exploits” the opportunities in the network. Existing System Such fixed path schemes fail to take advantages of broadcast nature and opportunities provided by the wireless medium and result in unnecessary packet retransmissions. The opportunistic routing decisions, in contrast, are made in an online manner by choosing the next relay based on the actual transmission outcomes as well as a rank ordering of neighboring nodes. Opportunistic routing mitigates the impact of poor wireless links by exploiting the broadcast nature of wireless transmissions and the path diversity. Proposed System We investigate the problem of opportunistically routing packets in a wireless multi-hop network when zero or erroneous knowledge of transmission success probabilities and network topology is available. Using a reinforcement learning framework, we propose an adaptive opportunistic routing algorithm which minimizes the expected average per packet cost for routing a packet from a source node to a destination.
  • 2. Our proposed reinforcement learning framework allows for a low complexity, low overhead, distributed asynchronous implementation. The most significant characteristics of the proposed solution are: • It is oblivious to the initial knowledge of network. • It is distributed; each node makes decisions based on its belief using the information obtained from its neighbors. • It is asynchronous; at any time any subset of nodes can update their corresponding beliefs. IMPLEMENTATION Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective. The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods. Modules: Algorithm used d-Adaptor Initialization stage We consider the problem of routing packets from a source node o to a destination node d in a wireless ad-hoc network of d + 1 nodes denoted by the set _ = fo; 1; 2; : : : ; dg. The time is slotted and indexed by n _ 0 (this assumption is not technically critical and is only assumed for ease of exposition). A packet indexed by m _ 1 is generated at the source node o at time _m s according to an arbitrary distribution with rate _ > 0.
  • 3. Transmission Stage We assume a fixed transmission cost ci > 0 is incurred upon a transmission from node i. Transmission cost ci can be considered to model the amount of energy used for transmission, the expected time to transmit a given packet, or the hop count when the cost is equal to unity. Acknowledgement Message Passing We discriminate amongst the termination events as follows:We assume that upon the termination of a packet at the destination (successful delivery of a packet to the destination) a fixed and given positive reward R is obtained, while no reward is obtained if the packet is terminated (dropped) before it reaches the destination. Relay Stage Given a successful transmission from node i to the set of neighbor nodes S, the next (possibly randomized) routing decision includes 1) retransmission by node i, 2) relaying the packet by a node j 2 S, or 3) dropping the packet all together. If node j is selected as a relay, then it transmits the packet at the next slot, while other nodes k 6= j; k 2 S, expunge that packet. We define the termination event for packet m to be the event that packet m is either received by the destination or is dropped by a relay before reaching the destination.
  • 4. H/W System Configuration:- Processor - Pentium –III Speed - 1.1 Ghz RAM - 256 MB(min) Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA Software Requirements: • Operating system :- Windows XP Professional • JDK :-1.5/ 1.6 and above • Front End :- JAVA, Swing(JFC),