ARBR: Adaptive reinforcement-based routing for DTN

Ahmed Elwhishi, Pin-Han Ho, K. Naik, Basem Shihada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations


This paper introduces a novel routing protocol in Delay Tolerant Networks (DTNs), aiming to solve the online distributed routing problem. By manipulating a collaborative reinforcement learning technique, a group of nodes can cooperate with each other and make a forwarding decision for the stored messages based on a cost function at each contact with another node. The proposed protocol is characterized by not only considering the contact time statistics under a novel contact model, but also looks into the feedback on user behavior and network conditions, such as congestion and buffer occupancy sampled during each previous contact with any other node. Therefore, the proposed protocol can achieve high efficiency via an adaptive and intelligent routing mechanism according to network conditions. Extensive simulation is conducted to verify the proposed protocol, where a comparison is made with a number of existing encounter-based routing protocols in term of the number of transmissions of each message, message delivery delay, and delivery ratio. The results of the simulation demonstrate the effectiveness of the proposed technique.
Original languageEnglish (US)
Title of host publication2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Print)9781424477432
StatePublished - Oct 2010

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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