Although most smartphones today have both cellular data and WiFi capacity, network-selection techniques typically only allow for a single data interface to be used at a time. This leads to several problems including the interruption of existing connections when switching interfaces, complete loss of connectivity when the selected network is not functioning correctly, and significantly less bandwidth to the device than is possible if the available wireless capacity is aggregated. While interface bonding is not new, aggregating two such diverse networks is particularly challenging given that different wireless technologies, and even different networks of the same type, offer inconsistent link parameters, presenting dynamically fluctuating bandwidth, latency, and packet loss. In this paper we present a simple, but extremely efficient, hashing technique for multi-interface packet scheduling. We have implemented our system with aggregation proxies running in well-connected data centers and client code running on a Galaxy Nexus running Android 4.1. Our experimental results show that our prototype never loses connectivity even when networks fail as long as at least one network is functioning, does not break connections as interface selection is adjusted, and can increase bandwidth to the smartphone to the available aggregated network capacities. Transmission time is reduced by up to 60% vs. using a single network interface.

Delay-tolerant networks (DTNs) have the potential to interconnect devices in regions that current networking technology cannot reach. To realize the DTN vision, routes must be found over multiple unreliable, intermittently-connected hops. In this paper we present a practical routing protocol that uses only observed information about the network. We designed a metric that estimates the average waiting time for each potential next hop. This learned topology information is distributed using a link-state routing protocol, where the link-state packets are "flooded" using epidemic routing. The routing is recomputed each time connections are established, allowing messages to take advantage of unpredictable contacts. A message is forwarded if the topology suggests that the connected node is "closer" to the destination than the current node. We demonstrate through simulation that our protocol provides performance similar to that of schemes that have global knowledge of the network topology, yet without requiring that knowledge. Further, it requires significantly less resources than the alternative, epidemic routing, suggesting that our approach scales better with the number of messages in the network. This performance is achieved with minimal protocol overhead for networks of approximately 100 nodes.