Modern connected devices are equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Current network management solutions fail to provide a fine-grained, coordinated, and transparent answer to this heterogeneity, while the lower layers of the OSI stack simply ignore it by providing full separation of layers. To address this, we propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless networks and introduce capabilities such as packet-level dynamic and intelligent handovers (both inter-and intra-technology), load balancing, replication, and scheduling. The framework is the first of its kind in providing a fine-grained packet-level control across different technologies by introducing a fully transparent virtual MAC layer and an SDN-like controller with global intelligence. Furthermore, we present a novel optimization problem formulation that can be solved to optimally configure the network. We provide a thorough evaluation through simulations and a prototype implementation. We show that our framework enables, in a real-life setting, transparent and real-time inter-technology handovers and that coordinated load balancing can double the network-wide throughput across different scenarios.

Dynamic Spectrum Access allows using the spectrum opportunistically by identifying wireless technologies sharing the same medium. However, detecting a given technology is, most of the time, not enough to increase spectrum efficiency and mitigate coexistence problems due to radio interference. As a solution, recognizing traffic patterns may lead to select the best time to access the shared spectrum optimally. To this extent, we present a traffic recognition approach that, to the best of our knowledge, is the first non-intrusive method to detect traffic patterns directly from the radio spectrum, contrary to traditional packet-based analysis methods. In particular, we designed a Deep Learning (DL) architecture that differentiates between Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) traffic, burst traffic with different duty cycles, and traffic with varying rates of transmission. As input to these models, we explore the use of images representing the spectrum in time and time-frequency. Furthermore, we present a novel data randomization approach to generate realistic synthetic data that combines two state-of-the-art simulators. Finally, we show that after training and testing our models in the generated dataset, we achieve an accuracy of ≥ 96 % and outperform state-of-the-art methods based on IP-packets with DL.

Abstract-Local area networks (LANs) are employed by a plethora of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Existing solutions and the lower layers of the OSI stack are unfit to cope with this heterogeneity. For instance, dynamical inter-technology switching is user-of application-based. We propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless local area networks (WLANs) and introduce capabilities such as packetlevel dynamic and intelligent handovers (both inter-and intratechnology), load balancing, replication, and scheduling. The framework consists of a controller that is capable of communicating with both existing Software-Defined Networking (SDN) and Network Function Virtualization (NFV) controllers and with devices containing a newly introduced virtual Medium Access Control (MAC) layer. We show that the virtual MAC enables transparent and real-time inter-technology handovers and that our solution scales up to two thousands of clients.

The number of connected devices has reached 18 billion in 2017 and this will nearly double by 2022, while also new wireless communication technologies become available. Since these modern devices support the use of multiple communication technologies, efforts have been made to enable simultaneous usage and handovers between the different technologies for these devices. However, existing solutions are missing the intelligence to decide on fine-grained (e.g. flow or packet level) optimizations that can drastically enhance the network's performance (e.g., throughput) and user experience. To this extent, we present a multi-technology flow-management load balancing approach for heterogeneous wireless networks that dynamically re-routes traffic through heterogeneous networks, in order to maximize the global throughput. This dynamic approach can be deployed on top of existing solutions and takes into account the specific characteristics of the different technologies, as well as station mobility. We both present a mathematical problem formulation and a heuristic that ensures practical scalability. We demonstrate the heuristic's ability to increase the network-wide throughput by more than 100 % across a variety of scenarios and scalability up to 10000 devices.

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Traffic recognition is commonly done using Deep Packet Inspection or packet-based approaches. However, these methods require the listening device to be part of the network and raise privacy concerns. Traffic recognition models that operate directly at the spectrum level could, for instance, be used for smart spectrum management.To this extent, we present such an approach using deep learning methods. In particular, we present a Convolutional Neural Network architecture that forms the basis for prediction models to recognize different transport protocols, burst traffic with different duty cycles, and different transmission rates. These models are trained with pure synthetic data to lighten the burden of data collection. As such, we validate recent successes in the area of robotics in the context of wireless networks. We compare the performance of two different datasets that contain spectrum images in either time or time-frequency domain. Our evaluation shows that using time-domain data results in an accuracy of at least 96 % across all models. Time-frequency information improves this accuracy even further. Furthermore, a validation with real-life data shows that it is still possible to discriminate between different transmission rates with an accuracy of around 87 %, while the detection of duty cycles and transport protocols takes place with accuracies of, respectively, around 73 and 78 %. Finally, we also present a small-scale real-life prototype.