三维水下传感网部署与组网联合设计

项目名称： 三维水下传感网部署与组网联合设计

项目编号： No.61262074

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 刘建明

作者单位： 桂林电子科技大学

项目金额： 50万元

中文摘要： 克服水下不利因素进行可靠覆盖和组网是水下传感网(UWSN)研究的热点和难点。本项目研究一种三维分层混合部署模型，开发相应的路由和重构策略，研究部署结构和组网方案之间的影响机理。初始部署时，移动节点随机分布于水体顶部，形成簇头节点备选集，固定节点以体心立方格方式布置于水体内部，用最少的节点实现三维覆盖，并形成移动/固定节点之间的静态分层；移动节点将借助水流/重力/电能进行主动补位，修补由节点缺失引起的网络割裂和空洞区域，重构物理拓扑。本项目为分层部署结构开发"静态分层-动态分簇"路由方案，在簇头备选集中随机选择簇头，进行动态分簇组网，均衡节点能耗。对簇内路由，研究基于水深的前向转发节点路由算法，以降低通信冲突和能耗；研究基于水深的路径恢复机制，以克服空洞问题，并配合补盲重构技术得到高可靠性、延迟容忍的UWSN组网技术，实现部署结构、服务质量(覆盖范围、寿命、时延、吞吐量)之间的联合优化。

中文关键词： 水下传感网；静态分层部署；动态分簇路由；补盲重构；联合设计与优化

英文摘要： In the critical underwater environment,reliable deployment and networking of sensor networks are still open challenges. This proposal aims to develop a novel 3-D layered mixture model for underwater sensor networks (UWSN). The corresponding routing/reconfiguration schemes, the interaction between deployment and networking are also studied, as the integrated parts of this deployment strategy. On the initial moment, the mobile nodes are randomly scattered over the top layer of the target area, as the set of potential cluster-heads (PCH), while the fixed nodes are body-centered deployed inside the target area, achieving 3-D coverage with minimum nodes and two static layers of mobile and fixed nodes. The mobile nodes will actively fill the vacant position of fixed node with the aid of current/gravity/battery, fixing the disconnectivity or void due to the absence of fixed nodes. We will study the 'statically-layered/dynamic-clustered' routing scheme, which randomly picking up cluster-heads from the set of PCH to balance the energy consumption among mobile nodes. For intra-cluster routing, we will study the depth-based forwarding set selection algorithm, to reduce the transmission collisions and energy consumption. To escape the void area, we will develop a depth-based route recovery mechanism, together with the recon

英文关键词： under sensor network；statically layered deployment；dynamic clustered routing；re-configration；joint design and optimization