Background & Objective:
The next generation of the internet where physical things or
objects are going to interact with each other without human interventions is called the Internet of
Things (IoT). Its presence can improve the quality of human lives in different domains and environments
such as agriculture, smart homes, intelligent transportation systems, and smart grids.
:
In the lowest layer of the IoT architecture (i.e., the perception layer), there are a variety of sensors
which are responsible for gathering data from their environment to provide service for customers.
However, these collected data are not always accurate and may be infected with anomalies for
some reasons such as limited sensor’s resources and environmental influences.
:
Accordingly, anomaly detection can be used as a preprocessing phase to prevent sending inappropriate
data for the processing.
Methods:
Since distributed characteristic and its heterogeneous elements complicate the application
of anomaly detection techniques, in this paper, a cluster-based ensemble classification approach
has been presented.
Results & Conclusion:
Will possessing low complexity, the proposed method has high accuracy
in detecting anomalies. This method has been tested on the data collected from sensors in the Intel
Berkley research laboratory which is one of the free and available datasets in the domain of IoT.
The results indicated that the proposed technique could achieve an accuracy of 99.9186%, a positive
detection rate of 99.7459%, while reducing false positive rate and misclassification rate to
0.0025% and 0.0813% respectively.
Segmentation of retinal blood vessels based on feature-oriented dictionary learning and sparse coding using ensemble classification approach
