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Mobile Crowdsensing

Network measurements are of high importance both for the operation of networks and for the design and evaluation of new management mechanisms. Therefore, several approaches exist for running network measurements, ranging from analyzing live traffic traces from campus or Internet Service Provider (ISP) networks to performing active measurements on distributed testbeds, e.g., PlanetLab, or involving volunteers. However, each method falls short, offering only a partial view of the network. For instance, the scope of passive traffic traces is limited to an ISP's network and customers' habits, whereas active measurements might be biased by the population or node location involved. To complement these techniques, we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view of the Internet and provide better control than do measurements with voluntary participants. In [1], we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations, and present best practices to overcome these issues. The contribution is a guideline for researchers to understand when and how to exploit crowdsourcing for network measurements.
In [2], distributed active network measurements via crowdsourcing are developed to increase the coverage of vantage points. Internet video constitutes more than half of all consumer traffic. Most of the video traffic is delivered by content delivery networks (CDNs). The huge amount of traffic from video CDNs poses problems to access providers. To understand and monitor the impact of video traffic on access networks and the topology of CDNs, distributed active measurements are needed. Recently used measurement platforms are mainly hosted in National Research and Education Networks (NRENs). However, the view of these platforms on the CDN is very limited, since the coverage of NRENs is low in developing countries. Furthermore, campus networks do not reflect the characteristics of end user access networks. We propose to use crowdsourcing to increase the coverage of vantage points in distributed active network measurements. In this study, we compare measurements of a global CDN conducted in PlanetLab with measurements assigned to workers of a crowdsourcing platform. Thus, the coverage of vantage points and the sampled part of the global video CDN are analyzed. Our results show that the capability of PlanetLab to measure global CDNs is rather low, since the vast majority of requests is directed to the US. By using a crowdsourcing platform we obtain a diverse set of vantage points that reveals more than twice as many autonomous systems deploying video servers.


[1] Hirth, M., Hoßfeld, T., Mellia, M., Schwartz, C., & Lehrieder, F. (2015). Crowdsourced network measurements: Benefits and best practices. Computer Networks, 90, 85-98.
[2] Burger, Valentin, et al. "Increasing the Coverage of Vantage Points in Distributed Active Network Measurements by Crowdsourcing." Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance. Springer International Publishing, 2014. 151-161.