Reducing Congestion for Routing Algorithms in Opportunistic Networks with Socially-Aware Node Behavior Prediction

Abstract

Since mobile devices nowadays have become ubiquitous, several types of networks formed over such devices have been proposed. One such approach is opportunistic networking, which is based on a store-carry-and-forward paradigm, where nodes store data and carry it until they reach a suitable node for forwarding. The problem in such networks is how to decide which the next hop will be, since nodes do not have a global view of the network. An inefficient opportunistic routing algorithm can lead to the congestion of a network, because same groups of nodes send messages between each other, without the destination actually receiving the data (or receiving it with a high delay). We describe here a routing algorithm for opportunistic networks that avoids congestion and the overcrowding of nodes, by routing messages only to nodes that have a high chance of reaching a message's destination. This is performed with the use of social networks and node behavior prediction. We show that our algorithm outperforms existing algorithms such as BUBBLE Rap in terms of delivery cost and hit rate, as well as the rate of congestion introduced in the networks.