Genetic Improvement of Routing Protocols for Delay Tolerant Networks

TL;DR

This paper applies Genetic Improvement to enhance the delivery probability of routing protocols in Delay Tolerant Networks, demonstrating consistent improvements without significant drawbacks across multiple urban network scenarios.

Contribution

It introduces a novel application of Genetic Improvement to optimize existing DTN routing protocols, specifically Epidemic and PRoPHET, by manipulating their core components.

Findings

GI improves delivery probability in 4 out of 6 urban network cases

The optimized protocols maintain comparable latency and buffer times

Analysis reveals logical differences between baseline and evolved protocols

Abstract

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic "store and forward" replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.