激励节点合作行为并抑制其自私行为对移动Ad Hoc网络而言至关重要，声誉系统和价格系统是目前最主要的两类解决方案，但两类系统中存在的阈值体系、聚合方案和奖励机制等问题往往导致它们无法单独提供有效的合作激励。本课题将深入研究该环境下的节点合作激励策略，构建一种价格系统辅助的声誉管理系统，研究内容包括：建立事件监测和信息采集机制，在较低网络开销下获取高质量的声誉评价信息和服务交易信息；研究基于分布式哈希表的信息收集和查询系统，避免节点间频繁的信息交换，保证信息可信传播和聚合；构建声誉管理模型，准确计算节点声誉值并具备较强的抗声誉失真攻击能力；设计声誉自适应的账户信用管理机制，解决信用循环问题，保障声誉计算和价格计算的公平性；新系统可为依附网络提供正向反馈，将其应用于普适社交网中以进一步保障网络基本功能的安全性和可用性。最终本课题将采用系统仿真与实际原型应用相结合的研究方案对上述内容进行验证。

Motivating cooperation behaviors between nodes and restricting selfish behaviors are important for proper operations of mobile ad hoc networks (MANETs). For this purpose, most previous countermeasures rely on either reputation systems or price systems. However, these systems are not sufficiently effective in providing incentives for node cooperation separately, due to the threshold mechanism, aggregation scheme, reward method and etc. This project will study the variety of incentive strategies for node cooperation in MANETs, and try to construct a price system-aided reputation management system. Our researches include the following five aspects: in order to obtain the high-quality information for reputation evaluation and service transaction under the premise of low network overhead, we first put forward an effective mechanism for event monitoring and information collection; we subsequently construct a system for information aggregation and information query on the basis of distributed hash table theory, which is used to avoid frequent information exchange between nodes, and ensure reliable propagation and aggregation of information; then we propose a reputation management model, which can calculate an accurate reputation value for a specific node, and can have a strong ability for resisting reputation distortion attacks; furthermore, based on the reputation model, we design an account credit management mechanism. This adaptive mechanism solves the credit cycle problem, and realizes the fairness of reputation calculation and price calculation; with the guide of above theories, this new system is established, which provides positive feedback to the attachment network. We apply it to the pervasive social network to further enhance the security and availability of basic functions. Finally, the research contents are verified by the combination of system simulation and actual prototype application.