Smartphone-based geolocation of Internet hosts

The location of Internet hosts is frequently used in distributed applications and networking services. Examples include customized advertising, distribution of content, and position-based security. Unfortunately the relationship between an IP address and its position is in general very weak. This motivates the study of measurement-based IP geolocation techniques, where the position of the target host is actively estimated using the delays between a number of landmarks and the target itself. This paper discusses an IP geolocation method based on crowdsourcing where the smartphones of users operate as landmarks. Since smartphones rely on wireless connections, a specific delay-distance model was derived to capture the characteristics of this novel operating scenario

Design and implementation of an Android library for supporting network-aware applications

In the last years, research about context-aware systems has been particularly intense. Nevertheless, most of the proposed approaches and systems failed to flow from research to the industrial world. We propose ANARC a library that eases the development of network aware applications for smartphones. ANARC does not try to cope with all the possible meanings and variations of context, it instead focuses on a specific restriction of context: the network and associated properties. To make things easier for designers and developers, ANARC adopts a rule and trigger based approach: when the network context matches the one described in a rule, the corresponding notification is sent to the application level. Examples of use of the proposed library are also included

An IP Geolocation Approach Based on Smartphones of a Mobile Crowdsourcing System

In the recent years the attention of the scientific community has been focused on the analysis of the Internet Topology, in order to understand how the interconnections between Internet nodes works. We already know that the core of the actual Internet is a multi-tier hierarchy of IP transit providers. We also know that many reasearch project exist with the only purpose of investigating the Internet Topology. One of these project, called The Portolan Project, developed in the IT departement of the University of Pisa, is currently active and is giving a great contribution on the discovery of Internet Topology due to his bottom-up approach to the problem and the use of a mobile crowdsourcing system through Android smartphones. Another interesting field comed out in this last years is the IP Geolocation, i.e. the geographical localization of Logical IP addresses on the Internet. The motivations that focus the interest around this topic are various: the pop up of new location-aware applications like smartphones apps, web site contents and advertisement or the importance of finding the sources of malwares and viruses, or even spammers, or else academic studies on the way people use the Internet and more. There are various projects dedicated to the IP Geolocation, since it is not an easy task: there is no direct relationship between the IP address of a host and its geographic location. These projects, of academic or commercial nature, try to find a way to reach a great accuracy in the IP geolocation in order to give a full working service to the users. The contribution of this thesis is to develop an IP geolocation approach using the tools provided by the Portolan Project and the Spotter project, and to apply it in order to make the Internet Topology Analysis more complete. The experiments carried out in this thesis work will give in the future the possibility to develop a tool to geolocate IP addresses on the Internet in the Portolan Android App available to all the users

Large-Scale Networks: Algorithms, Complexity and Real Applications

Networks have broad applicability to real-world systems, due to their ability to model and represent complex relationships. The discovery and forecasting of insightful patterns from networks are at the core of analytical intelligence in government, industry, and science. Discoveries and forecasts, especially from large-scale networks commonly available in the big-data era, strongly rely on fast and efficient network algorithms. Algorithms for dealing with large-scale networks are the first topic of research we focus on in this thesis. We design, theoretically analyze and implement efficient algorithms and parallel algorithms, rigorously proving their worst-case time and space complexities. Our main contributions in this area are novel, parallel algorithms to detect k-clique communities, special network groups which are widely used to understand complex phenomena. The proposed algorithms have a space complexity which is the square root of that of the current state-of-the-art. Time complexity achieved is optimal, since it is inversely proportional to the number of processing units available. Extensive experiments were conducted to confirm the efficiency of the proposed algorithms, even in comparison to the state-of-the-art. We experimentally measured a linear speedup, substantiating the optimal performances attained. The second focus of this thesis is the application of networks to discover insights from real-world systems. We introduce novel methodologies to capture cross correlations in evolving networks. We instantiate these methodologies to study the Internet, one of the most, if not the most, pervasive modern technological system. We investigate the dynamics of connectivity among Internet companies, those which interconnect to ensure global Internet access. We then combine connectivity dynamics with historical worldwide stock markets data, and produce graphical representations to visually identify high correlations. We find that geographically close Internet companies offering similar services are driven by common economic factors. We also provide evidence on the existence and nature of hidden factors governing the dynamics of Internet connectivity. Finally, we propose network models to effectively study the Internet Domain Name System (DNS) traffic, and leverage these models to obtain rankings of Internet domains as well as to identify malicious activities

On the feasibility of measuring the internet through smartphone-based crowdsourcing

The large base of Internet-enabled smartphones provides an excellent opportunity for a fine-grained observation of the structure of the Internet and a quantitative evaluation of its characteristics. Smartphones can operate as active mobile monitors and, coordinated by a central entity, they can probe the network on a local scale. Then the results produced by a large number of participants can be merged to obtain a detailed graph of the Internet. Besides the design of the measurement framework, this paper describes the implementation and validation of a traceroute-like tool that is compatible with the Android platform. This confirms that smartphone-based crowdsourcing of network properties can be a viable strategy