Abstract
Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.
Highlights
The world has seen the incredible growth in the Internet as a global communication infrastructure in recent decades
This paper implements an end-to-end network traffic classification system based on a fiber wireless access network by mapping an Ethernet passive optical network (EPON)
We collected a smart environment dataset with 21 unique Internet of Things (IoT) devices, analyzed the trace file and extracted the traffic behavior features
Summary
The world has seen the incredible growth in the Internet as a global communication infrastructure in recent decades. The wired and wireless Internet revolutionized the telecommunications paradigm to enable communication with anyone, “anytime.” The emerging Internet of Things (IoT) is creating another paradigm, in which “anything” can be accessed and/or controlled remotely, allowing for a more direct coordination between the physical world and machines-based systems [1]. IoT refers to billions of Internet-connected physical devices worldwide, collecting and sharing data. There will be a rapid increase in the number of different pieces of IoT equipment, as sensors and actuators are widely used in many applications, such as cyber security, automation, metering, health care, utilities and consumer electronics. The world’s IoT devices are expected to reach
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