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Description: With the rapid growth of the Internet of Things (IoT), the
deployment, management, and identification of IoT devices that are connected
to networks become a big concern. Consequently, they emerge as a
prominent challenge either for mobile network operators who try to offer
cost-effective services tailored to IoT market, or for network administrators
who aim to identify as well reduce costs processing and optimize
traffic management of connected environments. In order to achieve high
accuracy in terms of reliability, loss and response time, new devices real
time discovery techniques based on traffic characteristics are mandatory
in favor of the identification of IoT connected devices.
Therefore, we design GBC−IoT, a group-based machine learning approach
that enables to identify connected IoT devices through network
traffic analysis. By leveraging well-known machine learning algorithms,
GBC−IoT framework identifies and categorizes IoT devices into three
classes with an overall accuracy equals to roughly 99.98%. Therefore,
GBC−IoT can efficiently identify IoT devices with less processing overhead
compared to previous studies.
Platform: |
Size: 827167 |
Author: elmustafasayed@gmail.com |
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Description: Designed for both reference and teaching with clear mathematical and physical formulations of far-field optical microscopy techniques.
Contains English translations of seminal papers that are historically and pedagogically relevant.
Gives detailed tutorials on many optical topics that are underdeveloped in similar books.
Discusses the ethical dimensions of the discovery of each relevant microscope.
Platform: |
Size: 18528767 |
Author: nr1jack |
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