Measurement-Based Design of Roadside Content Delivery Systems

With today's ubiquity of thin computing devices, mobile users are accustomed to having rich location-aware information at their fingertips, such as restaurant menus, shopping mall maps, movie showtimes, and trailers. However, delivering rich content is challenging, particularly for highly mobile users in vehicles. Technologies such as cellular-3G provide limited bandwidth at significant costs. In contrast, providers can cheaply and easily deploy a small number of WiFi infostations that quickly deliver large content to vehicles passing by for future offline browsing. While several projects have proposed systems for disseminating content via roadside infostations, most use simplified models and simulations to guide their design for scalability. Many suspect that scalability with increasing vehicle density is the major challenge for infostations, but few if any have studied the performance of these systems via real measurements. Intuitively, per-vehicle throughput for unicast infostations degrades with the number of vehicles near the infostation, while broadcast infostations are unreliable, and lack rate adaptation. In this work, we collect over 200 h of detailed highway measurements with a fleet of WiFi-enabled vehicles. We use analysis of these results to explore the design space of WiFi infostations, in order to determine whether unicast or broadcast should be used to build high-throughput infostations that scale with device density. Our measurement results demonstrate the limitations of both approaches. Our insights lead to Starfish, a high-bandwidth and scalable infostation system that incorporates device-to-device data scavenging, where nearby vehicles share data received from the infostation. Data scavenging increases dissemination throughput by a factor of 2-6, allowing both broadcast and unicast throughput to scale with device density.