IEEE Signal Processing - May 2018 - 49
dynamics for efficient data acquisition and smart environmental
control. Typically, a CPS consists of a large number of sensor
nodes and actuator nodes that, respectively, monitor and control
a physical plant by transmitting data to an elaboration node
called the local controller (LC)/FC.
Traditional environmental information-monitoring approach-
es take sensing samples at a predefined speed uniformly at
power-constrained sensor nodes and then report the data to an
LC/FC, which is normally powerful and capable of handling
complex computations. The data transmitted to the LC/FC usu-
ally have redundancies that can be exploited to reduce power
consumption for data transmission. A common and efficient
method is to compress data at each individual sensor node and
then transmit them. However, data compression introduces addi-
tional power consumption for individual sensor nodes, although
the power consumption for data transmission is reduced. Fur-
thermore, this approach is unsuitable for real-time applications,
owing to the high latency in data collection and the high compu-
tational complexity in executing a compression algorithm at the
power-constrained sensor node.
We note that most natural signals can be transformed to a
sparse domain, such as a discrete cosine domain or wavelet
domain, where a small number of the coefficients can represent
most of the power of the signals that used to be represented by
a large number of samples in their original domains. In fact,
the data collected at each sensor node show a sparsity property
in the discrete cosine domain or the wavelet domain due to the
temporal correlation. Inspired by this, CS can be applied at each
sensor node to collect the compressed measurements directly
and then send them to the LC/FC or the neighbor nodes. As a
result, fewer measurements are sampled and transmitted, and
the corresponding power consumption is significantly reduced.
Sensor node power consumption is mainly from sensing,
data processing, and communications with the LC/FC. In a
large-scale WSN, low-power sensor nodes seek to take samples
at a lower speed or even revert to sleep mode to extend their
life span. As the signal received at each sensor node shows
temporal correlation and neighboring nodes display spatial
correlation, the joint sparsity can be exploited to recover sig-
nals from all of the sensor nodes, even though samples from a
portion of the participating nodes are missed. The active sen-
sor nodes can be preselected according to their power levels,
so the life span of the sensor nodes and of the entire network
can be extended by using CS in WSNs. The existing work on
CS-enabled WSNs falls mainly into the aforementioned two
categories: data gathering and active node selection.
Data gathering
In the traditional data-gathering setup, there is a large number
of sensor nodes deployed in a WSN to collect monitoring data.
Each sensor node generates a reading periodically and then
sends it to the LC/FC. As the sensor nodes are generally lim-
ited in computational ability and energy storage, the WSN
data-gathering process should be energy efficient, with low
overhead, which becomes quite challenging in IoT scenarios
where a huge number of sensor nodes is deployed. Taking tem-
perature monitoring as an example, sensors will generate read-
ings similar to those at nearby locations. Furthermore, for each
sensor node, the readings from time-adjacent snapshots will be
close to each other. These two important observations indicate
the temporal-spatial correlations among temperature readings,
which enable the application of CS to reduce the network over-
head and extend the network lifetime. Moreover, such a joint
sparsity is smaller than the aggregate over the individual signal
sparsity, which results in a further reduction in the number of
required measurements to exactly recover the original signals.
Instead of applying compression to the data after they are
sampled and buffered, each sensor node collects the com-
pressed measurements directly by projecting the signal to its
sparse domain. At each individual sensor node, one can naive-
ly obtain separate measurements of its signal and then recover
the signal for each sensor separately at the LC/FC by utiliz-
ing the intrasignal correlation. Moreover, it is also possible to
obtain compressed measurements that are each a combination
of all of the signals from the cooperative sensor nodes in a
WSN. Subsequently, signals can be recovered simultaneously
by exploiting both the intersignal and intrasignal correlations
at the LC/FC.
Measurement matrix design
When adopting CS techniques for data gathering in WSNs,
sampling at uniformly distributed random moments satisfies
the RIP if the sparse basis W is orthogonal. For an arbi-
trary sensor node i, the P # N measurement matrix can be
a spike one that has only P number of nonzero items, as
shown by
R0 1 0 0 f 0V
S
W
S0 0 0 1 f 0W
.
(8)
Ui = S
j W
SS
WW
T0 0 0 0 f 1X
A sensor node will take a sample at the moment when the cor-
responding item in U i is 1.
However, random sampling is not proper for WSNs in prac-
tice, since two samples may be too close to each other, which
becomes very challenging for cheap sensor nodes. To solve
this issue, Chen and Wassell [7] proposed a random-sampling
scheme by utilizing the temporal correlation of signals received
at a sensor node. In the proposed scheme, the sensor node sends
a pseudorandom generator seed to the FC and then sends out
the samples that are obtained at an affordable highest rate until
a sampling rate indicator (SRI) is received from the FC. Here,
the SRI is decided based on the recovery accuracy calculated
at the FC. Once the recovery accuracy extends to the required
range, the sensor node gradually increases its sampling rate
until the recovery error becomes acceptable. By adopting such
a scheme, the sensor node adjusts its sampling rate adaptively
without knowledge of the sparsity level. To further reduce the
sampling rate at sensor nodes, spatial correlation is exploited
in combination with temporal correlation. Therefore, the joint
sparsity property is utilized at the FC to reduce the number of
required measurements.
IEEE Signal Processing Magazine
|
May 2018
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49
Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018
Contents
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