Energy efficient sensor networks using
analog signal processing
Wide-scale
deployment of wireless sensor networks for envisioned applications in
surveillance and monitoring has been inhibited primarily by the lack of ability
to last for long durations on small power sources, such as batteries and
energy-harvesting systems. Any effort to reduce the communication overhead by
performing local processing results in increasing the energy spent on
computation, and viceversa. In order to break this
deadlock and make substantial improvements in the lifetime of a sensor network,
there is a critical need for local computation to be available at low power,
which would then allow decisions to be made locally and data to be compressed
into pertinent information. Meeting this need would relieve the sensor nodes of
constant-communication requirements and the associated high power-consumption
costs.
In this
project, our objective is to develop
wireless sensor networks that are capable of increased computational ability at
each sensor node while simultaneously increasing the longevity of operation. To
do so, we will capitalize upon on the low-power and parallel computation
available with programmable analog circuitry, which we will use in concert with
sensor node (motes).
We have
performed preliminary studies of combining analog signal processing with
wireless sensor networks [1] in which we used analog circuits to perform
spectral decomposition of a signal while consuming < 3μW, which is
far less than the power consumed by a Crossbow TelosB mote in its lowest-power
sleep mode. Compared with standard design practices for optimizing the
processing-communication tradeoff that often yield only incremental
improvements in energy savings, the analog signal processing based approach
promises substantial improvement in the performance and longevity of the
system.
Publications
o
B.
Rumberg, D. Graham, V. Kulathumani,
“Hibernets: Energy efficient sensor networks using analog signal
processing”, 1(3), 321—334, IEEE JETCAS, 2011
o
B.
Rumberg, D. Graham, V. Kulathumani, “Hibernets: Energy efficient sensor networks using analog signal
processing”, IPSN 2010, Stockholm, Sweden