IEEE Signal Processing - May 2018 - 51
Active node selection
In large-scale WSNs, the events are relatively sparse in com-
parison with the number of sensor nodes. Because of the power
constraint, it is unnecessary to activate all of the sensor nodes
all of the time. By utilizing the sparsity property constructed by
the spatial correlation, the number of active sensor nodes in
each time slot can be significantly reduced without harming
performance. Taking a smart monitoring system as an example,
as shown in Figure 6, the number of source nodes is N, and
there are K ^K % N h sparse events that are generated by the N
source nodes. By invoking CS, only M ^ M # N h active sensor
nodes are required to capture the K sparse events.
Centralized node selection
The researchers in [43] proposed a centralized node selection
approach by applying CS and matrix completion at the LC/
FC with the purpose of optimizing the network throughput
and extending sensor lifetime. As a node is either active or
sleeping, the state index for a node becomes binary, i.e.,
X ! " 0, 1 , . While conventional node selection in WSNs
focuses on only the spatial correlation of sensor nodes, Chen
and Wassell [44] exploited the temporal correlation by using
the support of the data reconstructed in the previous recovery
period to select the active nodes. Specifically, the FC performs
an optimized node selection, which is formulated as the
design of a specialized measurement matrix, where the sens-
ing matrix U consists of selected rows of an identity matrix,
as shown in (8).
The sensing costs of taking samples from different sensor
nodes are assumed to be equal in most of the node selection
approaches. However, in WSNs with power-constrained sensor
nodes, this assumption does not hold, because of the differ-
ent physical conditions at different sensor nodes. For example,
to extend the WSN lifetime, it is preferable to activate sensor
nodes with adequate energy rather than those almost out of
energy. Therefore, Chen and Wassell proposed a cost-aware
node selection approach in [45] to minimize the sampling
cost of the whole WSN, with constraints on the reconstru-
ction accuracy.
strategy with the purpose of minimizing the number of pack-
ets to be transmitted.
Generally speaking, the drawbacks for the distributed node
selection approach are the following: 1) The optimized node
selection requires an iterative process, which may require
a long period of time. 2) The flexibility to vary the number
of active sensor nodes is limited, especially according to the
dynamic sparsity levels or the channel conditions, which could
be time-varying. But, in the centralized approach, extra band-
width resources and power consumption are required to coor-
dinate the active sensor nodes.
Potential research
Even though researchers have carried out extensive studies to
investigate the application of CS in WSNs, most of them have
focused on reducing power consumption at sensor nodes and
extending the network lifetime. However, in large-scale WSNs
for different IoT applications, big data should be exploited to
enhance the CS recovery accuracy in addition to further re-
ducing the power consumption.
Machine learning-aided
adaptive measurement matrix design
Even though different applications entail different constraints,
the core concept for sparse representation remains the same.
Therefore, it is natural to ask if there is a general framework
for the sparse representation of the data in different 5G and IoT
applications for urban scenarios. To take the minimal number
of samples from the set of sensor nodes with the best capabili-
ty, i.e., highest power levels, the measurement matrix should
be properly designed. It has been demonstrated [74] that
machine learning can be an efficient tool to aid the measure-
ment matrix design so that the lifetime of the whole network as
well as of each individual sensor node can be extended to the
utmost. Furthermore, we should note that, when designing a
measurement matrix, the possibility of its implementation in a
Decentralized node selection
In contrast to DCS, which normally conducts signal recovery
at the FC by utilizing the data collected from the distributed
sensing sources via exploiting the joint sparsity, decentralized
CS-enabled WSNs aim to achieve in-network processing for
node selection. Long and Tian proposed a decentralized
approach in [46] to perform node selection by allowing each
active sensor node to monitor and recover only its local data
by collaborating with its neighboring active sensor nodes
through one-hop communication and iteratively improving the
local estimation until reaching the global optimum. It should
be noted that an active sensor node optimizes not only for
itself but also for its inactive neighbors. Moreover, to extend
the network lifetime, Caione et al. developed an in-network
CS framework [47] by enabling each sensor node to make an
autonomous decision on the data compression and forward
Active Node
Link in Centralized Network
Inactive Node
Link in Decentralized Network
FIGURE 6. The node selection in CS-based WSNs.
IEEE Signal Processing Magazine
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May 2018
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51
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