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.
S. Datta and S. K. Madria, "Prioritized Content Determination and Dissemination using Reinforcement Learning in DTNs," IEEE Transactions on Network Science and Engineering, Institute of Electrical and Electronics Engineers (IEEE), Apr 2021.
The definitive version is available at https://doi.org/10.1109/TNSE.2021.3072911
Center for High Performance Computing Research
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
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13 Apr 2021