Abstract-The localization of Internet hosts opens space for a wide scope of applications, from targeted, location aware content provision to localizing illegal content. In this paper we present a novel probabilistic approach, called Spotter, for estimating the geographic position of Internet devices with remarkable precision. While the existing methods use landmark specific calibration for building their internal models we show that the delay-distance data follow a generic distribution for each landmark. Hence, instead of describing the delay-distance space in a landmark specific manner our proposed method handles all the calibration points together and derives a common delay-distance model. This fundamental discovery indicates that, in contrast to prior techniques, Spotter is less prone to measurement errors and other anomalies such as indirect routing. To demonstrate the robustness and the accuracy of Spotter we test the performance on PlanetLab nodes as well as on a more realistic reference set collected by CAIDA explicitly for geolocation comparison purposes. To the best of our knowledge, we are the first to use this novel ground truth containing over 23000 network routers with their geographic locations.

Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.

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P4 is a high level language for programming network switches that allows for great flexibility in the description of packet structure and processing, independent of the specifics of the underlying hardware. In this demo, we present our prototype P4 compiler in which the hardware independent and hardware specific functionalities are separated. We have identified the requisites of the latter, which form the interface of our target specific Hardware Abstraction Library (HAL); the compiler turns P4 code into a target independent core program that is linked to this library and invokes its operations. The two stage separation improves portability: to support a new architecture, only the hardware dependent library has to be implemented. In the demo, we demonstrate the flexibility of our compiler with a HAL for Intel DPDK, and show the packet processing and forwarding performance of compiled switches in different scenarios.

This study outlines two novel techniques which can be used in the area of IP geolocation. First we introduce a detailed path-latency model to be able to determine the overall propagation delays along the network paths more accurately. This knowledge then leads to more precise geographic distance estimation between network routers and measurement nodes. In addition to the application of the detailed path-latency model, we describe a method which utilizes high-precision one-way delay measurements to further increase the accuracy of router geolocation techniques. The precise one-way delay values are used as a "path-constraint" to limit the overall geographic distance between the measurement nodes. The approach introduced in this paper can be used to localize all the network routers along the network path between the measurement nodes and can be combined with other existing geolocation techniques. The introduced techniques are validated in a wide range of experiments performed in the ETOMIC measurement infrastructure.

Abstract-To manage and monitor their networks in a proper way, network operators are often interested in identifying the applications generating the traffic traveling through their networks, and doing it as fast (i.e., from as few packets) as possible. Stateof-the-art packet-based traffic classification methods are either based on the costly inspection of the payload of several packets of each flow or on basic flow statistics that do not take into account the packet content. In this paper we consider the intermediate approach of analyzing only the first few bytes of the first (or first few) packets of each flow. We propose automatic, machinelearning-based methods achieving quite good early classification performance on real traffic traces generated from a diverse set of applications (including several versions of P2P TV and file sharing), while requiring only limited computational and memory resources.