In this research paper, a framework has been proposed to guarantee Quality-of-Service for vehicular networks, considering an environment where many types of services are simultaneously offered to the users. This proposed framework needs to be robust, cooperative and secure in nature. For having such characteristics in a solution, three modules have been proposed and amalgamated. First module takes care of Quality-of-Services assurance using a multi-constraint Quality-of-Service aware routing algorithm. This routing algorithm involves an innovative use of link-robustness parameter in an improved Firefly algorithm. Second module stimulates cooperation among vehicles using an incentive scheme evolved with the help of game-theoretic concepts like Core and Grand Coalition. It encourages vehicles to participate in network related activities and to obey network protocols. Third and the last module ensures the security of the presented framework by applying an authentication model. Integrated together as a framework, these modules orchestrate a unique and unified solution, probably first of its kind, that guarantees Quality-of-Service and at the same time, stimulates cooperation among vehicles and strengthens the robustness of a relatively secure vehicular network. Results suggest that the Quality-of-Service could be reasonably assured by the proposed framework for different traffic types such as multimedia, voice and background data.

在本研究论文中，考虑到同时向用户提供多种类型的服务的环境，提出了一个框架来保证车载网络的服务质量。这个提议的框架本质上需要是稳健的、合作的和安全的。为了在解决方案中具有这样的特性，已经提出并合并了三个模块。第一个模块使用多约束服务质量感知路由算法处理服务质量保证。该路由算法涉及在改进的 Firefly 算法中对链路鲁棒性参数的创新使用。第二个模块使用在 Core 和 Grand Coalition 等博弈论概念的帮助下发展的激励计划来刺激车辆之间的合作。它鼓励车辆参与网络相关活动并遵守网络协议。第三个也是最后一个模块通过应用身份验证模型确保所呈现框架的安全性。这些模块作为一个框架集成在一起，编排了一个独特而统一的解决方案，可能是同类解决方案中的首创，它可以保证服务质量，同时刺激车辆之间的合作并增强相对安全的车辆网络的稳健性。结果表明，针对多媒体、语音和背景数据等不同流量类型，所提出的框架可以合理地保证服务质量。这些模块编排了一个独特而统一的解决方案，可能是同类解决方案中的首创，它可以保证服务质量，同时刺激车辆之间的合作并加强相对安全的车辆网络的稳健性。结果表明，针对多媒体、语音和背景数据等不同流量类型，所提出的框架可以合理地保证服务质量。这些模块编排了一个独特而统一的解决方案，可能是同类解决方案中的首创，它可以保证服务质量，同时刺激车辆之间的合作并加强相对安全的车辆网络的稳健性。结果表明，针对多媒体、语音和背景数据等不同流量类型，所提出的框架可以合理地保证服务质量。