We investigate a transport layer protocol design that integrates 3G and WiFi networks, specifically targeting vehicular mobility. The goal is to move load from the expensive 3G network to the less expensive WiFi network without hurting the user experience. As the test platform we choose a nationwide 3G network and a commercially operated metro-scale WiFi network. We exploit the often complementary characteristics of these networks for a hybrid design at the transport layer. To this end, we modify the stock Linux SCTP implementation to support `striping' across multiple interfaces and the ability to handle frequent path failures and recovery in a seamless fashion. Instead of simply striping data over two network connections, we develop a utility and cost-based formulation that decides the right amount of load that can be put on the 3G network to maximize the user's benefit. We develop and experiment with a transport level scheduler to do this. We call the new SCTP design as oSCTP, meaning `SCTP to be used for offloading.' We demonstrate the effectiveness of oSCTP and show that it is able to deliver superior network throughput and user experience, while significantly reducing the load on the 3G network.

We perform a head-to-head comparison of the performance characteristics of a 3G network operated by a nation-wide provider and a metro-scale WiFi network operated by a commercial ISP, from the perspective of vehicular network access. Our experience shows that over a wide geographic region and under vehicular mobility, these networks exhibit very different throughput and coverage characteristics. WiFi has frequent disconnections even in a commercially operated, metro-scale deployment; but when connected, indeed delivers high throughputs even in a mobile scenario. The 3G network offers similar or lower throughputs in general, but provides excellent coverage and less throughput variability. The two network characteristics are often complementary. It is conceivable that these properties can be judiciously exploited for a hybrid network design where 3G data can be offloaded to WiFi for better performance and to reduce 3G network congestion and to lower costs.

Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost.

With the proliferation of WiFi technology, many WiFi networks are accessible from vehicles on the road making vehicular WiFi access realistic. However, several challenges exist: long latency to establish connection to a WiFi access point (AP), lossy link performance, and frequent disconnections due to mobility. We argue that people drive on familiar routes frequently, and thus the mobility and connectivity related information along their drives can be predicted with good accuracy using historical information – such as GPS tracks with timestamps, RF fingerprints, and link and network-layer addresses of visible APs. We exploit such information to develop new handoff and data transfer strategies. The handoff strategy reduces the connection establishment latency and also uses pre-scripted handoffs triggered by change in vehicle location. The data transfer strategy speeds up download performance by using prefetching on the APs yet to be encountered. Experimental performance evaluation reveals that the predictability of mobility and connectivity is high enough to be useful in such protocols. In our experiments with a vehicular client accessing road-side APs, the handoff strategy improves download performance by roughly a factor of 2 relative to the state-of-the-art. The data transfer strategy further improves this performance by another factor of 2.5.

We use a steerable beam directional antenna mounted on a moving vehicle to localize roadside WiFi access points (APs), located outdoors or inside buildings. Localizing APs is an important step towards understanding the topologies and network characteristics of large scale WiFi networks that are deployed in a chaotic fashion in urban areas. The idea is to estimate the angle of arrival of frames transmitted from the AP using signal strength information on different directional beams of the antenna - as the beam continuously rotates while the vehicle is moving. This information together with the GPS locations of the vehicle are used in a triangulation approach to localize the APs. We show how this method must be extended using a clustering approach to account for multi-path reflections in cluttered environments. Our technique is completely passive requiring minimum effort beyond driving the vehicle around in the neighborhood where the APs need to be localized, and is able to improve the localization accuracy by an order of magnitude compared with trilateration approaches using omnidirectional antennas, and by a factor of two relative to other known techniques using directional antennas.