Browsing by Subject "SDN"

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  • Hätönen, Seppo; Huque, Tanvir Ishtaique ul; Rao, Ashwin; Jourjon, Guillaume; Gramoli, Vincent; Tarkoma, Sasu (2020)
    Devices capable of multi-connectivity currently use static rules for selecting the set of interfaces to use. Such rules are limited in scope and can be counter-productive. We posit that SDN techniques can address this inefficiency. We present an approach that enables an SDN controller to manage the flows traversing the Ethernet, Wi-Fi, and LTE links in our laptop and also migrate the flows from one link to another. Our solution opens avenues that enable end-user device to negotiate with the network controllers when taking its control plane decisions.
  • Li, Yuhong; Su, Xiang; Ding, Aaron Yi; Lindgren, Anders; Liu, Xiaoli; Prehofer, Christian; Riekki, Jukka; Rahmani, Rahim; Tarkoma, Sasu; Hui, Pan (2020)
    The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.
  • Liu, Yanhe; Ding, Aaron Yi; Tarkoma, Sasu (University of Helsinki, Department of Computer Science, 2013)
    Department of Computer Science, Series of Publications C
  • Alhilal, Ahmad Yousef; Finley, Benjamin; Braud, Tristan; Su, Dongzhe; Hui, Pan (2022)
    Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.