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New ideas in optimization
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Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
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ICGEC '10 Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing
A Multiple-Data Multicast Routing Protocol for DTN
ICGEC '10 Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Journal on Selected Areas in Communications
Editorial: Complex dynamic networks: Tools and methods
Computer Networks: The International Journal of Computer and Telecommunications Networking
CGrAnt: a swarm intelligence-based routing protocol for delay tolerant networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper presents a new prediction-based forwarding protocol for complex and dynamic delay tolerant networks (DTNs). The proposed protocol is called GrAnt (Greedy Ant), as it uses the Ant Colony Optimization (ACO) metaheuristic with a greedy transition rule. This allows GrAnt to select the most promising forwarder nodes or allow for the exploitation of previously found good paths. The main motivation for using ACO is to take advantage of its population-based search and the rapid adaptation of its learning framework. Considering data from heuristic functions and pheromone concentration, the GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first unicast protocol that employs a greedy ACO and that (1) infers best promising forwarders from nodes' social connectivity, (2) determines the best paths a message must follow to eventually reach its destination while limiting message replications and droppings, and (3) performs message transmission scheduling and buffer space management. GrAnt is compared to the Epidemic and PROPHET protocols in two different mobility scenarios: one activity-based scenario (Working Day) and another based on Points of Interest. Simulation results obtained by the ONE simulator show that, in both scenarios, GrAnt achieves a higher delivery ratio, lower message redundancy, and fewer dropped messages than Epidemic or PROPHET.