A Load Balanced Social-Tie Routing strategy for DTNs based on queue length control

Delay Tolerant Networks (DTNs) are sparse mobile ad-hoc networks in which there is typically no complete path between the source and destination. Although many routing algorithms for DTNs have been proposed, prior works generally focus on optimizing delivery ratio and cost by finding the relay node with the highest delivery probability to the destination. In social networks, the connections among nodes, which are established via social contacts, exhibit a fat-tailed distribution in which few nodes have many connections and the majority have very few. Since current heuristic-based routing algorithms bias toward connectivity, highly connected nodes have a high probability to be selected by other nodes as their next hop. As a result, the load distribution becomes significantly unbalanced, with very few nodes handling the majority of message forwardings. In this paper, we provide empirical experiments to show the natural load imbalance of existing routing algorithms. We then introduce Load Balanced Social-Tie Routing (LBR), a routing strategy in which messages are favorably forwarded to network nodes that have both a stronger social tie with the destination and a smaller or similar queue length. This queue length control strategy aims to reduce traffic at highly connected nodes, and allows nodes of similar degrees to trade packet forwardings with each other, thus spreading the traffic more evenly across the network. Through extensive simulation studies using a real-world San Francisco cab trace, we show that LBR can achieve a comparable or better delivery ratio and cost than existing algorithms. Meanwhile, LBR distributes the load more evenly with the top 10% of network nodes handling 23% of the forwardings, compared to 37% for Epidemic routing, 43% for PROPHET, and 47% for BubbleRap.