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Distributed
Signal Processing in Sensor Networks
The
paradigm of point-to-point communications is well established, and dates
back to the seminal work of Shannon. With the recent advances of the network
technology, a new paradigm for signal processing and communication is emerging
and this will have a dramatic impact on the way we acquire and process
signals and the way we transport and reconstruct them. In our vision, multi-sensor
networks are given by a large number of low-power, inexpensive, smart devices
with multiple on-board sensors, connected through wireless links and the
internet. The locations of the sensors may be known or unknown and their
number may vary with time as sensors die or more are added. The realization
of the potential of sensor networks requires the identification and solution
of many new fundamental problems. In particular, since sensors are low-power
devices and communication is critical, there are new trade-offs between accuracy
of acquisition, delay, computational and transmission power that need
to be investigated. The main problems we plan to address include: understanding
of distributed data acquisition schemes and structures, distributed versus
centralized processing, fusion of the distributed sensed data to infer the
original physical phenomenon, joint vs separate source-channel coding.
Main Publications:
Collaborations and Interactions:
M. Vetterli (EPFL), M. Gastpar (UC, Berkeley) and K. Ramchandran
(UC, Berkeley)
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