The first wave of sensor network deployments from the early 2000s relied on aggregation-a strategy in which readings are combined locally using low-power radio links before they are communicated to the gateway. Aggregation reduced dependence on battery-draining, long-distance radio links, and reduced redundancy among reported data. We are now experiencing a second wave of sensor network research driven by ubiquitous smartphone usage. In this paper, we study the application of aggregation to the new smartphone sensor network setting, arguing that it can help reduce costs in contexts where existing cost-reduction strategies, such as opportunistic use of Wi-Fi and data piggybacking, do not apply. In more detail, we propose two new aggregation protocols, designed for the challenges of high mobility, that offer trade-offs in terms of bandwidth and energy savings. We then evaluate these protocols using both test bed experimentation (using a collection of 11 Samsung Galaxy Nexus smartphones running a Noise Tube-like application) and trace based simulation (using a large collection of mobility traces from taxi cabs in Singapore). Our experiments demonstrate that our aggregation protocols reduce cellular bandwidth usage by up to 95% while losing less than 5% of the data. Moreover, in many common cases, our protocols also yield significant energy savings.