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- Title
Efficacious implementation of deep Q-routing in opportunistic network.
- Authors
Dalal, Renu; Khari, Manju
- Abstract
Opportunistic network (Oppnet) is consisting of wireless nodes, which follows the Bluetooth transmission range between nodes. These kinds of networks are capable to handle long delay during transmission of messages. Dynamic network topology and unstable connection between nodes makes routing difficult in Oppnet. Overall to provide efficient and reliable transmission of messages are such a crucial task in Oppnet. To resolve these issues; this article introduces reinforcement learning based on Deep Q-learning technique. In this approach, Q-value is used to find best current intermediate node for forwarding the packet from source Oppnet node to destination Oppnet node. This protocol is simulated on ONE tool and it is found that Q-Routing in Oppnet works more efficiently as compared to conventional protocols like Epidemic, Maximum Probability (Max-Prob), and Probability Routing Protocol using History of Encounters and Transitivity (PRoPHET). Proposed protocol utilized; 71% lesser energy consumption, 66% reduced overhead ratio, 61% lesser end-to-end delay, 51% enhanced throughput, and 10% higher delivery ratio as compared to PRoPHET protocol when variation in number of nodes.
- Subjects
REINFORCEMENT learning; END-to-end delay; DELAY-tolerant networks; ENERGY consumption
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2023, Vol 27, Issue 14, p9459
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-023-08442-z