A Rule-Based Greedy Ant (rGrAnt) Protocol for Networking Environments

This paper presents a rule-based system for the Greedy Ant Protocol (GrAnt), named rGrAnt. GrAnt uses the Ant Colony Optimization (ACO) meta-heuristic aiming to route traffic in complex and dynamic Delay Tolerant Networks. rGrAnt has been developed to provide the protocol the ability to extract information online from nodes' social connectivity, which can range from disconnected and sparse to highly connected networking environments. With this information, the proposed protocol can guide through its fuzzy/crisp rules the ACO routing module by deciding when to consider data from heuristic functions and/or pheromone concentration, which data can be incorporated in both heuristic and pheromone parameters, and if the message forwarding phase must be less or more restrictive. In nodes with low connectivity, the rules of rGrAnt indicate that the protocol must be less restrictive when forwarding messages, in order to make better use of the few available contacts. In contrast, in nodes with high connectivity, it is necessary to restrict forwarding to avoid overloading the same sets of nodes and links. rGrAnt is compared with GrAnt in three different movement models. Results show that, in the three models, rGrAnt achieves a higher delivery ratio than GrAnt.