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    杨倩, 罗娟, 刘畅. 基于上下文的VANET服务推荐中间件[J]. 计算机研究与发展, 2017, 54(9): 1992-2000. DOI: 10.7544/issn1000-1239.2017.20160640
    引用本文: 杨倩, 罗娟, 刘畅. 基于上下文的VANET服务推荐中间件[J]. 计算机研究与发展, 2017, 54(9): 1992-2000. DOI: 10.7544/issn1000-1239.2017.20160640
    Yang Qian, Luo Juan, Liu Chang. Context Based Service Recommendation Middleware in VANET[J]. Journal of Computer Research and Development, 2017, 54(9): 1992-2000. DOI: 10.7544/issn1000-1239.2017.20160640
    Citation: Yang Qian, Luo Juan, Liu Chang. Context Based Service Recommendation Middleware in VANET[J]. Journal of Computer Research and Development, 2017, 54(9): 1992-2000. DOI: 10.7544/issn1000-1239.2017.20160640

    基于上下文的VANET服务推荐中间件

    Context Based Service Recommendation Middleware in VANET

    • 摘要: 车载自组织网络(vehicle ad hoc network, VANET)作为智慧城市的重要组成部分,它需要为车辆安全、便捷交通及舒适驾驶提供众多的服务.目前针对车联网中服务发现的研究主要集中在服务发现质量和服务发现延时,但是随着VANET中服务数量和种类的增加,车联网中的信息激增问题变得越来越严重,因此如何按照个性化需求为用户推荐合适的服务成为目前车联网中亟需解决的问题.针对现有车联网中服务选择策略的不足,提出一种基于上下文的车联网服务推荐中间件体系结构,该中间件可以利用车辆丰富的上下文信息和用户的历史服务记录为用户推荐服务.利用离线分析方法,提出一种基于上下文的服务推荐方法,将既符合车辆上下文约束且满足用户偏好的服务推荐给用户.仿真结果表明,中间件推荐的服务合理且符合用户偏好,同时可以降低服务导致的绕路概率.

       

      Abstract: VANET (vehicle ad hoc network) is a very important part of smart city which is required to implement a myriad services related to vehicles safety, traffic efficiency and comfortable driving experience. The current researches on service discovery in VANET are mainly focused on quality and latency of service. But with the development of service number and service type in VANET, the information explosion in VANET is increasing seriously, so there is an urgent need for VANET to provide services considering users’ individual requirements. This paper presents a context based service recommendation middleware architecture for VANET which can recommend services for users based on vehicles’ rich context information and users’ service history. With offline theory, a context-based approach of service recommendation is provided. Only when services meet the vehicle’s context constraints and the users’ preference model, they could be recommended to the user. Experimental results show that the recommended services are reasonable and meet users’ preference, additionally, the detours probability caused by services can be reduced.

       

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