用于道路监测改进的多重虚拟扫描算法

计算机应用 ›› 2011, Vol. 31 ›› Issue (12): 3187-3190.

用于道路监测改进的多重虚拟扫描算法

苏攀览¹,陈良银²,张靖宇¹,袁平³

1. 四川大学计算机学院，成都 610065
2. 四川大学计算机学院，成都 610065
3. 重庆教育学院计算机科学系，重庆 400067

收稿日期:2011-06-14 修回日期:2011-08-09 发布日期:2011-12-12 出版日期:2011-12-01
通讯作者: 陈良银
基金资助:
四川省科技支撑计划项目;美国国家自然科学基金资助项目

Improved M-virtual scanning algorithm for road surveillance

SU Pan-lan¹,CHEN Liang-yin¹,ZHANG Jing-yu¹,YUAN Ping²

1. School of Computer Science,Sichuan University, Chengdu Sichuan 610065, China
2. Department of Computer Science,Chongqing Education College, Chongqing 400067, China

Received:2011-06-14 Revised:2011-08-09 Online:2011-12-12 Published:2011-12-01
Contact: CHEN Liang-yin

摘要/Abstract

摘要： 虚拟扫描算法不能充分利用节点数量，为了延长网络生命周期它必须建立在节点密集部署的基础上，以致平均目标发现时间延长。为此，基于低占空比无线传感器网络（WSN），结合虚拟扫描波的思想，提出一种用于道路监测的多重虚拟扫描算法。该算法通过定点、同位置多节点部署的方式，使节点依次分批工作，以延长网络生命周期。仿真实验表明，多重虚拟扫描算法与虚拟扫描算法相比网络生命周期延长了180%，能有效提升网络性能。

关键词: 道路监测, 虚拟扫描算法, 定点, 多节点部署, 分批次

Abstract: VIrtual Scanning Algorithm（VISA) is unable to fully take advantage of the number of nodes, in order to prolong the network lifetime, it must be built on the basis of dense nodes deployment which makes the time for finding average target increase. Therefore, based on low dutycycle Wireless Sensor Network (WSN) by combining the ideology of virtual scan wave, the Multiple VIrtual Scan Algorithm (M-VISA) was proposed for road surveillance. This algorithm adopted the way of fixing points, deploying the same location with multinodes to let the nodes to be worked in order and in batches, hence, the network lifetime could be greatly extended. Simulation result demonstrates that M-VISA can prolong network lifetime by 180% when compared with VISA, improving the network performance effectively.

Key words: road monitoring, VIrtual Scanning Algorithm (VISA), fixed point, multi-node deployment, batches

苏攀览陈良银张靖宇袁平. 用于道路监测改进的多重虚拟扫描算法[J]. 计算机应用, 2011, 31(12): 3187-3190.

SU Pan-lan CHEN Liang-yin ZHANG Jing-yu YUAN Ping. Improved M-virtual scanning algorithm for road surveillance[J]. Journal of Computer Applications, 2011, 31(12): 3187-3190.

[1]	张宇豪, 袁孟雯, 陆宇婧, 燕锐, 唐华锦. 面向动态事件流的神经网络转换方法[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3033-3039.
[2]	雷小康, 尹志刚, 赵瑞莲. 基于FPGA的卷积神经网络定点加速[J]. 计算机应用, 2020, 40(10): 2811-2816.
[3]	张毅刘钰然罗元. 基于视觉的手势识别方法及其在数字信号处理器上的实现[J]. 计算机应用, 2014, 34(3): 833-836.
[4]	谭家杰黄三伟邹常青. 正则有符号系数FIR滤波器优化算法[J]. 计算机应用, 2011, 31(06): 1727-1729.
[5]	周波杨剑王东平. 基于随机采样一致性算法的平面匹配方法[J]. 计算机应用, 2011, 31(04): 1053-1056.
[6]	包健余红明. 一种定点数权值神经网络的优化方法及其应用[J]. 计算机应用, 2009, 29(1): 230-233.
[7]	余重基，李际军. 一种基于RBF网络生成人脸表情的算法[J]. 计算机应用, 2005, 25(07): 1611-1615.