Contact-based simulations are a very popular tool for the analysis of opportunistic networks. They are used for evaluation of networking metrics, for quantifying the effects of infrastructure and for the design of forwarding strategies. However, little evidence exists that the results of such simulations accurately describe the performance of opportunistic networks, as they commonly ignore some important factors (like limited transmission bandwidth) or they rely on assumptions such as infinite user cache sizes. In order to evaluate this issue, we design a testbed with a real application and real users; we collect application data in addition to the contact traces and compare measured performance to the results of the contact-based simulations. We find that contact-based simulations significantly overestimate delivery ratio, while the captured delay tends to be 2-3 times lower than the experimentally obtained delay. We show that assuming infinite cache sizes leads to misinterpretation of the effects of backbone on an opportunistic network. Finally, we show that contact traces can be used to analytically estimate the delivery ratios and the impact of backbone, through the dependency between a user centrality measure and her delivery ratio.

The increase in data consumed by smartphones is becoming a huge problem for mobile operators. In three years, mobile data traffic in AT&T's network rose 5000%. The US operators invest $50 billion in the data networks every year and the technology upgrades and innovation still fail to keep up with the demand. In this paper we design two algorithms for delay-tolerant offloading of bulky, socially recommended content from 3G networks. The first one, called "MixZones", uses opportunistic, ad hoc transfers between users, and is assisted by predictions made by the network operator. The second one, called "HotZones", exploits delay tolerance and tries to download contents when users are close to Wi-Fi access points; it is also assisted by predictions made by the operator. We evaluate both algorithms using a large data set, obtained from a major mobile operator and a realistic application similar to Apple's Ping music social network. The metrics address the amount of offloading, delay and mobile energy efficiency. We find that both solutions succeed in offloading a significant amount of traffic, with a positive impact on user battery lifetime. Surprisingly, we also find that all the benefit obtained from the operator with the MixZones algorithm (i.e with ad hoc exchanges between users) can be achieved with the HotZones algorithm and a small investment in Wi-Fi access points. Note that the latter is considerably less complex to deploy than the former.