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Security-Aware Resource Allocation for Mobile Social Big Data: A Matching-Coalitional Game Solution

机译:安全感知移动社会大数据的资源分配:匹配 - 联盟游戏解决方案

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As both the scale of mobile networks and the population of mobile users keep increasing, the applications of mobile social big data have emerged where mobile social users can use their mobile devices to exchange and share contents with each other. The security resource is needed to protect mobile social big data during the delivery. However, due to the limited security resource, how to allocate the security resource becomes a new challenge. Therefore, in this paper we propose a joint match-coalitional game based security-aware resource allocation scheme to deliver mobile social big data. In the proposed scheme, first a coalition game model is introduced for base stations (BSs) to form groups to provide both wireless and security resource, where the resource efficiency and profits can be improved. Second, a matching theory based model is employed to determine the selecting process between communities and the coalitions of BSs so that mobile social users can form communities to select the optimal coalition to obtain security resource. Third, a joint matching-coalition algorithm is presented to obtain the stable security-aware resource allocation. At last, the simulation experiments prove that the proposal scheme outperforms other existing schemes.
机译:随着移动网络的两个规模和移动用户的人口不断增加,移动社交大数据的应用已经出现在那里的移动社交用户可以使用他们的移动设备交换和共享内容与对方。需要安全资源的传递过程中保护移动社交大数据。然而,由于有限的安全资源,如何分配资源的安全成为一个新的挑战。因此,在本文中,我们提出了一个联合的比赛,联盟型游戏基于安全意识的资源分配方案,以提供移动社交大数据。在所提出的方案,第一联合游戏模型引入用于基站(BS),以形成基团,以提供无线和安全资源,其中所述资源利用效率和利润可以得到改善。其次,匹配理论基于模型被用来确定社区和基站的联盟之间的选择过程,使移动社交用户可以形成社区选择最佳的联盟,以获得安全性的资源。第三,联合匹配的联盟算法,以获得稳定的安全意识的资源分配。最后,仿真实验证明,该建议方案优于其他现有的计划。

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