Prioritized Content Determination and Dissemination using Reinforcement Learning in DTNs


In a battlefield, several groups of soldiers are deployed with different missions by the command and control center (CC). To continue the missions appropriately and get a better understanding of the situation, the soldiers as well as the CC need to collect information of interest generated in different battle zones using Delay-tolerant Networks (DTN). It is a challenge to determine the topics of interest associated with the events and missions, and efficiently forward the associated content to the CC in this extreme situation. We design a scheme to forward contents generated by the nodes to the CC using Reinforcement Learning (RL) while maximizing the number of interesting data in the respective nodes buffer, and avoiding congestion. In this forwarding process, we focus on identifying the trending topics/keywords among changing missions and their related data at the node level, and the changes of interest of the nodes based on their mobility and connectivity patterns of nodes in DTN. Experiments are conducted using real datasets and ONE simulator to show the effectiveness of Reinforcement Learning (RL) on the prioritized content dissemination in DTN networks.


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research


Published online: 13 Apr 2021

This research is partially supported by a AFRL grant.

Keywords and Phrases

Congestion; Delay Tolerant Network; Delays; Heuristic algorithms; Node interest; Peer-to-peer computing; Prediction algorithms; Reinforcement learning; Reinforcement Learning; Routing; Servers; Trending Topic

Document Type

Article - Journal

Document Version


File Type





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Publication Date

January/February 2022