Outlier Detection Techniques For Wireless Sensor Networks: A Survey · 5
(2006) employ a cell-based network architecture to locally detect events based on
collaboration among neighboring nodes. Luo et al. (2006) take into account dif-
ferent level of sensor fault probability during event detection. Krishnamachari and
Iyengar (2004) propose a distributed Bayesian protocol to detect event regions in
presence of faulty sensors. Ding et al. (2005) attempt to identify event boundaries
since detection of event boundary may become more important than detection of
event region because of unreliability of sensor measurements.
An essential difference between event detection and outlier detection is that out-
lier detection techniques have no a priori knowledge of trigger condition or semantic
of any event, while event detection techniques hold the trigger condition or seman-
tic of certain event issued by the sink node. Outlier detection aims at identify-
ing anomalous readings by comparing sensor measurements with each other, while
event detection aims at specifying a certain event by comparing sensor measure-
ments with the trigger condition or pre-defined pattern. On the one hand, outlier
detection techniques need to prevent normal data to be classified as outlier and
thus keeping the detection rate high and false alarm rate low, while event detection
techniques need to prevent erroneous data which conform to the event condition
or pattern to influence reliability of the detection. On the other hand, the com-
mon characteristic of outlier detection and event detection is that they employ
spatio-temporal correlations among sensor data of neighboring nodes to distinguish
between events and errors. This is based on the fact that noisy measurements and
sensor faults are likely to be stochastically unrelated, while event measurements are
likely to be spatially correlated (Luo et al., 2006).
Due to the fact that not all outliers have to be identified in event detection
applications, outlier detection techniques have not really been used in the literature
of event detection domain, although they may be suitable. In this paper, we focus
on addressing outlier detection in WSNs, excluding the discussion on the detections
of specific outlier sources and events.
2.4 Challenges of Outlier Detection in WSNs
Extracting useful knowledge from raw sensor data is not a trivial task (Tan, 2006).
The context of sensor networks and the nature of sensor data make design of an
appropriate outlier detection technique more challenging. Due to the following
reasons, conventional outlier detection techniques might not be suitable for handing
sensor data in WSNs.
— Resource constraints. The low cost and low quality sensor nodes have stringent
constraints in resources, such as energy, memory, computational capacity and
communication bandwidth. Most of traditional outlier detection techniques have
paid limited attention to reasonable availability of computational resources. They
are usually computationally exp ensive and require much memory for data analysis
and storage. Thus, a challenge for outlier detection in WSNs is how to minimize
the energy consumption while using a reasonable amount of memory for storage
and computational tasks.
— High communication cost. In WSNs, the majority of the energy is consumed for
radio communication rather than computation. For a sensor node, the commu-
nication cost is often several orders of magnitude higher than the computation
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