移动低占空比传感网邻居发现算法  被引量:13

Neighbor Discovery Algorithm in Mobile Low Duty Cycle WSNs

在线阅读下载全文

作  者:陈良银[1] 颜秉姝[1] 张靖宇[1] 胡剑波[1] 刘振磊[1] 刘燕[2] 徐正坤[3] 罗谦[4] 

机构地区:[1]四川大学计算机学院 [2]北京大学软件与微电子学院 [3]中国人民解放军78020部队 [4]中国民用航空总局第二研究所信息技术分公司

出  处:《软件学报》2014年第6期1352-1368,共17页Journal of Software

基  金:国家自然科学基金(61373091;60933011;11102124);国家重大基础研究发展计划(973)(2011CB302902);教育部新世纪优秀人才计划项目(NCET-10-0604);四川省科技支撑计划(2013SZ0002)

摘  要:低占空比技术极大地降低了传感网(即无线传感器网络)的能耗,延长了网络的生命周期,但却使邻居发现变得异常困难.尤其结合了节点移动性后,邻居发现问题将具有更大的挑战性.提出了一种基于Continuous Torus Quorum的移动低占空比无线传感器网络的邻居发现算法,可以解决这种在对称和非对称场景下的邻居发现问题,并提出了适用于移动场景的邻居发现概率作为评估邻居发现算法的性能,项目还开发了用于测量移动场景下低占空比邻居发现算法性能的仿真平台.理论分析和仿真实验结果均表明:该算法无论在对称或者非对称场景下均取得了很好的能效、发现概率和发现延时性能,优于当前几种典型的异构邻居发现算法(比如Disco,U-Connect等).Low duty cycle is proposed to reduce the energy consumption of WSNs (wireless sensor networks), thereby extending the lifecycle of WSNs. However, low duty cycle makes neighbor discovery extremely difficult. Especially considering the mobility of nodes, effective neighbor discovery is more challenging. In this work, a new neighbor discovery algorithm based on Continuous Torus Quorum is proposed to solve the neighbor discovery problem in asynchronous symmetric and asymmetric low duty cycle WSNs. A neighbor discovery probability is also provided to estimate efficiency of neighbor discovery algorithms in mobile scene. Furthermore, a simulation platform is developed to measure performance of neighbor discovery algorithms. Both theoretical analysis and simulation results reveal that Continuous-Torus-Quorum-based algorithm can achieve significant performance improvement over several classical heterogeneous neighbor discovery algorithms, such as Disco and U-Connect, in terms of energy efficiency, discovery delay and discovery probability in the symmetric and asymmetric scenes.

关 键 词:低占空比无线传感器网络 邻居发现算法 基于法定人数的连续算法 平均发现延迟 发现概率 

分 类 号:TP393[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象