Adaptive Opportunistic
Routing for Wireless Ad Hoc Networks
ABSTRACT:
A distributed
adaptive opportunistic routing scheme for multihop 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 that optimally “explores” and “exploits” the
opportunities in the network.
ARCHITECTURE:
EXISTING SYSTEM:
Motivated by classical routing solutions
in the Internet, conventional routing in ad hoc networks attempts to find a
fixed path along which the packets are forwarded. Such fixed-path schemes fail
to take advantage of broadcast nature and opportunities provided by the
wireless medium and result in unnecessary packet retransmissions.
DISADVANTAGES OF 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:
In the
proposed system, we develop a distributed adaptive opportunistic routing scheme
(d-AdaptOR) for multi-hop wireless ad hoc networks whose
performance is shown to be optimal with zero knowledge regarding network
topology and channel statistics.
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.
ADVANTAGES OF PROPOSED SYSTEM:
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.
MODULES:
·
Initialization stage
·
Transmission Stage
·
Acknowledgement Message Passing
·
Relay Stage
MODULES
DESCRIPTION:
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.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard
Disk : 40 GB.
•
Floppy
Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse : Logitech.
•
Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
•
Operating system : - Windows XP.
•
Coding Language : JAVA
REFERENCE:
Abhijeet A. Bhorkar, Mohammad Naghshvar,
Tara Javidi, and Bhaskar D. Rao,” Adaptive Opportunistic Routing
for Wireless Ad Hoc Networks” IEEE/ACM
TRANSACTIONS ON NETWORKING, VOL. 20, NO. 1, FEBRUARY 2012.
