23-04-2012, 01:19 PM
Optimization of Data Routing, Power Controlling in Wireless Sensor Networks
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INTRODUCTION
the general model for the network and traffic
flowing through the network allowing for
multicast of data generated at the sensor nodes to a
set of sink nodes. This scenario can be useful in
sensor networks. But, our problem can be
considered as a generalization of multihop wireless
networks where sleep and wake option may or may
not be there and modes may not be harvesting
energy. We define a mode of the network as a
possible combination of active links along with
their transmit powers such that any node is
communicating with only one other node over a half
duplex channel. The source of energy and the
energy harvesting device may be such that the
energy cannot be generated at all times (e.g., a solar
cell). Thus, we need efficient energy management
policies to modify the energy consumption profile
of the sensor node so as to achieve the desired
objectives with the given energy harvesting source.
MODEL
We consider an adhoc mesh network of sensor
nodes. Each sensor node senses a random field and
generates packets to be transmitted to a sink node
via the network of sensor node. The system is
slotted. During slot k (defined as time interval
[k , k + 1] ,i.e;a slot is unit of time) , k x bits are
generated by the sensor node. Although the sensor
node may generate data as packets, arbitrary
fragmentation of packets during transmission is
allowed (Zigbee allows byte level fragmentation;
arbitrary fragmentation provides a reasonable
approximation).
SIMULATION RESULTS
Take an example of a network having 5 nodes, 12
links and 6 flows and simulated both the proposed
distributed algorithms
Simulation Results for Concave Utility Algorithm
here we are calculating the lambda which is
nothing but fairness constraint. It is nothing but the
ratio between the permissible data rate to the total
transmission rate. The fairness of the sesnor
CONCLUSIONS AND FUTURE WORK
We optimized the power control, routing and
scheduling for the network subject to the network
constraints and proposed a distributed algorithm for
solving the problem in an iterative manner. We
compared the performance of the three approaches
using examples. It solves the congestion problems
in networks. So we may implement new congestion
control methods to improve the fairness by using
this method.
network will depends on the the value of lamda.