A Signaling Game for Uncertain Data Delivery in Selfish Mobile Social Networks

Abstract

Cooperative data delivery among mobile nodes can improve the performance of data delivery in mobile social networks. However, data routing in the presence of socially selfish (SS) nodes is challenging, where they mitigate the degree of their cooperation level based on their social features and ties to achieve their social objectives. This issue becomes more challenging when they prevent revealing their reactions about incoming messages, which leads data forwarding under uncertain behavior. In this paper, we propose a signaling game approach, namely, Sig4UDD, to study the impact of uncertain cooperation among well-behaved and SS nodes on the performance of data forwarding. In Sig4UDD, we employ Bayesian Nash equilibrium to analyze one-stage interactions among nodes. Then, perfect Bayesian equilibrium is applied to analyze their multistage interactions. In this stage, we establish a belief system to help SS nodes predict the type of their opponents and take appropriate actions to maximize their utilities. To update the beliefs of SS nodes, we devised the weighted social distance metric to measure the global social distance among nodes. Finally, we compare the performance of Sig4UDD to some benchmark cooperative and noncooperative data forwarding protocols using Reality Mining and Social Evolution data sets.