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different data source rates. Thus, for some sensor network
applications, it is possible to adjust source rate to achieve
overall energy efficiency of the sensor network.
From the simulation results above, it can be concluded
that there are some coupled relationships between source
rate control and relay power control in the effects on the
performances of total energy consumption and total data
throughput. On one hand, the relay node n2 should allocate
its relay power ratio properly so as to guarantee the transmission
of relaying traffic; on the other hand, the source
node n1 should be noticed to adjust its source data rate
so that the lifetime of relay nodes can be prolonged. This
motivates us to develop a joint power control and rate
adaptation scheme for wireless sensor network.
In this paper, we extend the above example to a general
sensor network case and study how each relay node determine
its relay power ratio and how each source node adapt
its transmit data rate. We will model the problem of data
gathering and transport in WSNs as a concave maximization
problem and present a pricing-based distributed algorithm,
with which each node can optimally control its
power consumption and adjust the source rate relayed by
it.
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