NDLI: Incentive-Compatible Packet Forwarding in Mobile Social Networks via Evolutionary Game Theory

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Li Feng Qinghai Yang Kyung Sup Kwak
Abstract	In the absence of end-to-end paths and without the knowledge of the whole network, packet forwarding, including forwarding decision (i.e., forwarding or dropping the packet) and relaying selection, is crucial to be made by the individual of the node based on the packet-forwarding protocol in autonomous mobile social networks (MSNs). In this paper, we investigate the adaptive packet forwarding in MSNs afflicted with potential selfish nodes. When considering the various selfish behaviors of network nodes in multi-hop MSNs, an incentive compatible multiple-copy packet forwarding (ICMPF) protocol is proposed to maintain a satisfied packet delivery probability while reducing the delivery overhead. Considering the fact that the node's forwarding decision in the ICMPF protocol is affected by its available resources (i.e., bandwidth and location privacy) and network environment (i.e., other nodes' actions and social ties), an evolutionary game framework is exploited for modeling the complicated interactions among nodes to guide their forwarding behaviors. Meanwhile, we portray the forwarding behavior dynamics and develop the evolutionary stable strategy (ESS) for this game-theoretic framework. Then, we prove that the strategy dynamics converge to the ESS and further develop a distributed learning algorithm for nodes to approach to the ESS. Simulation results show that our system converges to the ESS and also is robust to the learning error induced by the communication noise.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2689775
Journal	IEEE Access
Volume Number	5
Language	English
Publisher	IEEE
Publisher Date	2017-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Selfishness Evolutionary Game Theory Multiple-copy Packet Forwarding Protocol
Content Type	Text
Resource Type	Article

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