容迟网络能在深空、陆地、水下等极端通信环境中，提供数据感知、监测、收集、传输等服务。近几年，容迟网络中基于社交关系的路由研究备受国内外学者关注。这类路由主要依赖社交关系做路由决策，它们大多通过收集和分析网络状态信息来获取有状态社交关系，然而收集和存储大量的网络状态信息对容迟网络极具挑战性。本项目从无状态社交关系的角度研究容迟网络中路由问题，避免收集任何状态信息。主要研究内容包括：（1）从在线社交网络中爬取社交属性数据，并对数据的多样化和值缺失等问题进行处理；（2）使用社会网络分析、社会现象、无监督聚类算法等方法，从社交属性数据中获取无状态社交关系；（3）对数据传输过程建立优化模型，并研究满足容迟网络的激励机制，进而设计基于无状态社交关系的路由。本项目研究，不仅对容迟网络中数据传输效率的提高具有重要的理论意义，还为容迟网络的应用提供理论基础和技术支持，具有重要的应用价值。

Delay Tolerant Networks (DTNs) can provide sensing, monitoring, collection, transmission and other data services in the extreme environments, e.g., deep space, remote area, underwater, etc. In recent years, the research of social-aware routing protocol for DTNs attracts considerable attention from domestic and overseas scholars. These routings significantly rely on social ties, acquired by collecting and analyzing a large number of network status data, to make routing decisions. However, it is challenging for DTNs to obtain and store such data. For this purpose, this project aims at studying the routing problem of DTNs from the perspective of stateless social ties. Firstly, this project crawls social attribute data from online social networks and handles them. Secondly, this project uses some advanced methods, e.g., social network analysis, social phenomenon and unsupervised clustering algorithm, to get stateless social ties from the social attribute data. Thirdly, this project models the data transmission process, and studies the incentive mechanism meeting the requirements of DTNs. Build on them, this project designs stateless-social-aware routing protocols. In short, this project not only has important theoretical significance to improve the efficiency of data transmission in DTNs, but also has significant pratical value in providing a theoretical basis and technical support for the application of DTNs.