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Several works over the past few years have shown that the Internet AS-level topology is partially hidden from the current Internet measurement infrastructures. Most have focused on the incompleteness of the connectivity extracted from BGP data. A few have analysed the connectivity collected by traceroute measurement infrastructures showing the amount of connections introduced by traceroute campaigns. None, however, have investigated in detail the underlying rationale, i.e. the economic nature of the Internet. In this paper we fill this gap by analysing five traceroute infrastructures, found to be active in October 2013, with the p2c-distance metric, which is specifically designed to capture the complex economic dynamics that rule the Internet. We found that the traceroute infrastructures that currently run topology discovery measurements (Ark, DIMES and Portolan), together with BGP route collectors, are able to reveal the full connectivity of 23.50% of the Internet core ASes. This is a considerable improvement given that the BGP infrastructure alone is able to cover only 15.90% of the Internet core. This percentage could be increased up to 48.48% if the remaining two infrastructures (Dasu/Ono and RIPE Atlas) performed topology discovery campaigns. We also found that the placement of traceroute probes is not optimal from a topology discovery perspective, as it causes several probes to provide only redundant connectivity information. We show that the same number of traceroute probes optimally deployed, would be able to completely reveal the full AS connectivity of the Internet core.

Ubiquitousness of smartphones, when combined with the power of crowdsourcing, enables radically novel application scenarios, where a massive amount of mobile users scattered over wide geographical regions cooperate towards a single goal. Nevertheless these new possibilities come at the cost of additional complexity, such as the presence of humans in the control loop, scarce resources of mobile devices, increased management costs due the large number of users. In this paper we report and discuss the lessons learned from the design, implementation and management of Portolan, a smartphone-based crowdsourcing system aimed at monitoring large-scale networks

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