4.5 Article

An optimized message routing approach inspired by the landlord-peasants game in disruption-tolerant networks

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AD HOC NETWORKS
卷 127, 期 -, 页码 -

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DOI: 10.1016/j.adhoc.2022.102781

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The disruption-tolerant network (DTN); Routing; Incentive compatibility; Game theory; Mechanism design; Landlord-Peasants game

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This paper proposes a method inspired by the Landlord-Peasants game to increase collaboration between selfish nodes in disruption-tolerant networks. The simulation results show that the proposed method outperforms baseline methods and a state-of-the-art learning-based method in terms of delivery ratio, buffer usage, and number of hops, with a comparable overhead.
Disruption-tolerant networks (DTNs) have emerged as a paradigm shift for environments that may not have a continuous network connection. Although DTNs were originally designed for space-planned explorations, they can be used for crisis management in natural disasters such as earthquakes, fires, floods, etc. One of the major disadvantages of DTNs is the lack of sufficient motivation among nodes to forward messages. This problem can lead to degradation in network performance. In this paper, we propose a method to forward messages inspired by the Landlord-Peasants game in such a way that leads to more collaboration between selfish nodes. In our proposed method, each node joins one of the coalitions. In each coalition, the sender and receiver nodes play the role of landlord and peasant, respectively. Both landlord and peasant, upon relaying a message, receive reputation. In this way, both parties have a motivation to participate in forwarding operations. The simulation of the proposed algorithm shows its superiority compared to well-known baseline methods such as Direct Delivery, PRoPHET, Epidemic, and Spray&Wait in terms of delivery ratio, buffer usage, number of hops, etc. For the sake of fairness, we also compare the proposed method with a learning-based state-of-the-art method, called K-nearest neighbors routing. The results also show that the overhead of the proposed method is not much higher than that of baseline and state-of-the-art methods.

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