IEEE Signal Processing - May 2018 - 50
More recently, Kong et al. proposed a big-data-enabled
WSN framework [42] that invokes CS for data completion
with a minimal number of samples. The proposed data collec-
tion framework consists of two core components.
1) At the cloud, an online learning component predicts the
minimal data to be collected to reduce the amount for trans-
mission; these data are considered the principal ones, and
their amount is constrained by CS.
2) At each individual node, a local control component tunes
the collection strategy according to the dynamics that are
present and any unexpected environmental variation.
Combining these two components, this framework can reduce
power consumption and guarantee data quality simultaneously.
Anomaly detection
In CS-enabled data-gathering processing, abnormal sensor
readings may still lead to severe degradation in signal recov-
ery at the LC/FC, even if CS shows robustness to abnormal
sensor readings, as it does not rely on the statistical distribu-
tion of data to be preserved during runtime. This is because
abnormal readings will damage signals' sparsity property, as
shown in Figure 5. Therefore, it is critical to discover the abnor-
mal sensors to guarantee the security of WSNs and make them
abnormality-free.
Generally, abnormal readings are caused by either internal
errors or external events according to their specific patterns.
Abnormal readings due to internal errors fail to represent the
sensed physical data; thus, they should, at least, be removed.
But the abnormal readings caused by external events should be
preserved, as they reflect actual WSN scenarios.
Inspired by recovering data from an overcomplete dictionary,
an abnormality detection mechanism was proposed to enhance
the compressibility of the signals. First, the abnormal values are
detected with the help of recovering signals from an overcom-
plete dictionary. Second, the failing sensor nodes are categorized
into different types, according to their patterns. Third, the failing
nodes caused by the internal errors are removed, and then the
data recovery is carried out to obtain the ordinal data.
250
200
50
150
DCT Coefficients
Temperature Readings (°C)
100
0
100
50
0
-50
-50
-100
0
10
20
30
Reading Index
(a)
40
-100
50
10
20
30
DCT Coefficient Index
(b)
40
50
0
10
20
30
DCT Coefficient Index
(d)
40
50
250
100
200
50
150
DCT Coefficients
Temperature Readings (°C)
0
0
100
50
0
-50
-50
-100
0
10
20
30
Reading Index
(c)
40
50
-100
FIGURE 5. The effect of abnormal readings on the sparsity level of temperature readings in the discrete cosine domain: (a) the original signal, (b) the DCT
of the original signal, (c) an abnormal signal, and (d) the DCT of the abnormal signal.
50
IEEE Signal Processing Magazine
|
May 2018
|
Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018
Contents
IEEE Signal Processing - May 2018 - Cover1
IEEE Signal Processing - May 2018 - Cover2
IEEE Signal Processing - May 2018 - Contents
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IEEE Signal Processing - May 2018 - Cover3
IEEE Signal Processing - May 2018 - Cover4
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