Both Hypercube and deBruijn networks possess desirable properties. It should be understood, though, that some of the attractive features of one are not found in the other. The architecture proposed in this paper is a combination of these architectures, providing some of the desirable properties of both the networks such as admitting many computationally important networks, flexibility in terms of connections per node as well as level of fault-tolerance. Also the network allows a simple VLSI layout, scalability as well as decomposability. Thus, these networks can be a potential candidate for VLSI multiprocessor networks. The proposed network possesses logarithmic diameter, optimal connectivity, and simple routing algorithms amendable to networks with faults. Importantly, in addition to being pancyclic, these hyper-deBruijn networks admit most computationally important subnetworks including rings, multidimensional meshes, complete binary trees, and mesh of trees with perfect dilation.Techniques for optimal one-to-all (OTA) broadcasting in these networks are presented. As an intermediate result, this technique provides the fastest OTA broadcasting in binary deBruijn networks as well. The recent renewed interest in binary deBruijn networks makes this later result valuable.

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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.

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