Drop Policies for DTN Routing Protocols with Delivery Probability Estimation


Delay Tolerant Networks (DTN) are characterized by a lack of end-to-end connectivity. As such, messages (called bundles) can be stored in buffers for a long time. Network congestion can result in poor delivery rates, as bundles are dropped before having a chance of reaching their destination. Some routing protocols, such as MaxProp and Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET), maintain estimations of delivery probabilities for each destination. In this paper, a new drop policy called Largest Bundle’s Hosts Deliverability (LBHD) is proposed that considers all the hosts that received a replica of the same bundle, and their respective delivery probability as estimated by a routing protocol. LBHD uses this additional information to better manage congestion. Simulation results show that LBHD consistently achieves the best delivery probability when paired with PRoPHET and compared with other drop policies proposed in the literature. Also, when paired with MaxProp, LBHD shows the most efficient performance among all the other state of the art policies considering performance metrics such as average delay, overhead ratio and bundle delivery rate. In addition, another drop policy called One Hop Delivery Estimation Drop (OHDED) is proposed. OHDED takes advantage of the encounter predictions of every node in the network stored in every node when using MaxProp. By accurately predicting the bundles that have the highest probability of being delivered directly or in two hops, the results show the best performance in delivery rate and overhead ratio in high congestion scenarios.

How to Cite
RODRIGUES, Miguel Pinheiro; MAGAIA, Naércio; PEREIRA, Paulo Rogério. Drop Policies for DTN Routing Protocols with Delivery Probability Estimation. Journal on Advances in Theoretical and Applied Informatics, [S.l.], v. 3, n. 1, p. 16-24, aug. 2017. ISSN 2447-5033. Available at: <http://revista.univem.edu.br/jadi/article/view/2432>. Date accessed: 26 sep. 2017.