摘要
在sink移动轨迹固定的传感器网络中,由于sink点有限的通信时间和节点的随机分布,使得很难兼顾数据采集量的提高和整体能耗的降低.为了解决该问题,提出了一种最大数据量最短路径(maximum amount shortest path,简称MASP)数据采集方法.MASP对网络中成员节点与sub-sink节点之间的匹配关系进行集中式优化.采用0-1线性规划方法对MASP问题进行形式化描述,提出了一种基于二维染色体编码的遗传算法进行求解,并给出了相应的数据通信协议设计.另外,MASP可以扩展支持低密度网络和多sink点网络.基于OMNET++的仿真结果表明,MASP在能耗利用率方面要远远优于最短路径树方法(shortest path tree,简称SPT)及固定sink数据采集方法.
In sensor networks with a path-fixed mobile sink, due to the limited communication time of the mobile sink and random deployment of the sensor nodes, it is quite difficult to increase the amount of data collected and reduce energy consumption simultaneously. To address this problem, this paper proposes a data collection scheme called maximum amount shortest path （MASP） to optimize the mapping between members and sub-sinks. MASP is formulated as an integer linear programming problem which is solved by a genetic algorithm. A communication protocol is designed to implement MASP, which is also applicable in sensor networks with low density and multiple sinks. Simulations under OMNET＋＋ shows that MASP outperforms shortest path tree （SPT） and static sink methods in terms of energy utilization efficiency.
出处
《软件学报》
EI
CSCD
北大核心
2010年第1期147-162,共16页
Journal of Software
基金
国家重点基础研究发展计划(973)No.2007CB307100~~
关键词
传感器网络
移动SINK
轨迹固定
数据采集
能耗利用率
sensor network
mobile sink
path-constraint
data collection
energy utilization efficiency
