However, in wireless networks, the coexistence of different types of traffic requires that the communication characteristics and needs of the various devices be further differentiated. By contrast, wireless networks are more flexible and can provide service over larger areas than optical networks. Although optical networks can provide a wide bandwidth, it is not possible to deploy these networks everywhere because of their reliance on physical infrastructure and the associated deployment costs. The FiWi integrates the access network connects of the passive optical network (PON) and wireless local area network (WLAN) and takes advantage of both optical and wireless access. Fiber-wireless (FiWi) converged access networks are gaining more attention for their ability to handle this level of traffic in the ubiquitous community. The number of network users and the traffic load due to these users are gradually increasing. The rapid development of IoT devices presents a new problem of network allocation on the current Internet, especially the “last mile” access network, which has long been recognized as a major bottleneck in delivering high-speed internet service. Further, the world’s IoT devices are expected to reach 18 billion by 2022. Gartner predicts that, by 2022, the typical household could contain more than 500 smart devices. 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. IoT refers to billions of Internet-connected physical devices worldwide, collecting and sharing data. 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. The world has seen the incredible growth in the Internet as a global communication infrastructure in recent decades. 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. We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. 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.). 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). 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. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. The management of network resources for IoT service provisioning is a major issue in modern communication. 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).
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