以人为辅助的移动协同感知网络关键技术研究

项目名称： 以人为辅助的移动协同感知网络关键技术研究

项目编号： No.U1304615

项目类型： 联合基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 何欣

作者单位： 河南大学

项目金额： 30万元

中文摘要： 数据感知是连接物理世界与信息世界的桥梁，是人类实现信息化认知的源泉。当前信息的感知和传输主要依靠预先布控静态无线传感器网络来实现。由于感知节点固定，常难以满足人们多变的感知需求；而将经常移动中的人作为感知节点，对感知区域采取主动灵活的感知形式，则人们的感知信息多样化需求有望得到解决。为此，本项目拟以稀疏静态感知节点网络为基础，形成一个以移动节点代替大量静态节点为主要感知模式的移动协同感知网络；并以此网络为对象，建立一套相应的感知服务查询与传输机制。重点研究：⑴基于机会委托机制的协同感知服务模型，通过委托节点的优化选取，实现请求的委托传递；通过服务节点的协同感知，实现目标区域的覆盖，解决移动网络区域覆盖感知的难题；⑵利用机会路由机制，通过优化选择路由节点，实现感知数据有效投递，解决移动网络的通信问题；⑶基于感知服务过程中协同服务节点数、投递成功率等因素，构建优化函数，实现协作节点的优化选取。

中文关键词： 以人为辅助的移动协同感知网络；覆盖感知技术；路由技术；服务节点集优化选取；众包感知

英文摘要： Data-aware is not only a bridge to connect the physical world with the information world, and is the source of information cognition. At present, data-aware and communication depend on static wireless sensor networks deployed. The sense node is static and difficult to meet the people's flexible perception needs. If people is as mobile node and take active flexible sense way on target area, people's diversification perception needs is be met. Accordingly, based on the sparse static nodes network, the mobile cooperative sense network is introduced by mobile nodes instead of a large number of static nodes. And, the service query and transmission mechanism is established for the network. Main research contents including: (1) A cooperative sense service model based on opportunity delegate is proposed. Through of delegate nodes optimization selection, the query information is delivered by delegate nodes. The target area information is sensed by cooperative service nodes, and the coverage problem of mobile network is solved. (2) Through of route node optimization selection, sensing data is delivered based on opportunity route mechanism. The communication problem of mobile network is solved. (3) The optimization function is designed by using factors including the number of cooperative service nodes, delivery success rat

英文关键词： people-assisted mobile cooperative sensing network；coverage sensing；routing；Service node set selection；Crowd Sensing