摘要
感知网一般是由静态的或移动的节点组成,为保证感知网的感知功能,节点应该有自部署和自修复能力.然而全部由移动传感器组成的感知网的成本太高,为保证感知网的覆盖功能和低成本,提出了一种在静态传感器节点中加入移动传感器节点的混合感知网形式.为了更好地部署这些节点,最大化覆盖待感知区域,提出了一种基于节点间虚拟力的移动节点部署方法,利用静态节点和移动节点以及移动节点之间的虚拟人工势场产生的作用力来控制移动节点的运动,使移动节点能够在较短的时间内,以较少的能量消耗到达自己合适的位置.在理论上分析了算法的可行性,用仿真实验验证了此算法的有效性,并和其他3种类似算法进行了性能比较.
Most existing researches on sensor networks consider networks where all sensors are static nodes or mobile nodes. To ensure good performance, sensor networks should have self-deploying and self-healing capability to handle coverage holes caused by random locations and sensor failures. However, a mobile sensor has much higher cost than a static sensor with similar sensing capability, and deploying only mobile sensors in the network can cause the sensor cost too high. To improve the coverage performance in a sensor network while keeping the sensor cost low, it is proposed to intentionally add many mobile sensors to a number of static sensors in a sensor network. Mobile sensors can improve network performance by moving to locations where there is a coverage hole. Thus, mobile sensors can essentially provide self-healing and self-optimizing capabilities in sensor networks. A hybrid sensor neuwork is composed of static nodes and mobile nodes. A novel mobile nodes deployment method based on virtual force among nodes is presented in order to deploy these mobile nodes for forming maximum coverage of sensing area. The effect forces resulted from virtual potential fields between these nodes are utilized to control the movement of mobile nodes. This way makes mobile nodes move to appropriate positions using a little energy consumed in allowable time. The feasibility of the algorithm is analyzed in theory. Its validity is verified by numeric simulation and the performances are compared with that of other three similar algorithms.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2007年第6期965-972,共8页
Journal of Computer Research and Development
基金
国家"八六三"高技术研究发展计划基金项目(863-2001AA422270)
国家自然科学基金项目(69985002)~~
关键词
混合感知网
部署
虚拟力
多机器人
移动节点
hybrid sensor networks
deployment
virtual force
multi-robot
mobile node
