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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.

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