异步传感器网络随机事件捕获在线调度方案

doi:10.16180/j.cnki.issn1007-7820.2019.02.015

电子科技 ›› 2019, Vol. 32 ›› Issue (2): 70-74.doi: 10.16180/j.cnki.issn1007-7820.2019.02.015

异步传感器网络随机事件捕获在线调度方案

余志祥¹,程宗毛¹,徐向华²

^1. 杭州电子科技大学理学院,浙江杭州 310018
^2. 杭州电子科技大学计算机学院,浙江杭州 310018

收稿日期:2018-01-26 出版日期:2019-02-15 发布日期:2019-01-02
作者简介:余志祥(1991-),男,硕士研究生。研究方向:传感器网络中随机事件捕获和能量优化。|程宗毛(1964-),男,博士,副教授。研究方向:传感器网络中的数理统计和最优化。
基金资助:
国家自然科学基金(61370087);浙江省科技项目(2017C01065);浙江省科技项目(2017C01022)

An Online Scheduling Scheme for Random Event Capture in Asynchronous Sensor Networks

YU Zhixiang¹,CHENG Zongmao¹,XU Xianghua²

^1. School of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China
^2. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Received:2018-01-26 Online:2019-02-15 Published:2019-01-02
Supported by:
National Natural Science Foundation of China(61370087);Zhejiang Provincial Science and Technology Program(2017C01065);Zhejiang Provincial Science and Technology Program(2017C01022)

摘要/Abstract

摘要：

针对已有异步传感器网络中依据随机事件的随机特性进行节点休眠调度离线问题,给出描述事件随机特性随机变量分布参数的Bayes估计。讨论了瞬时捕获概率、传感器捕获事件能量效率和分布参数的Bayes估计值之间的关系,得到了传感器休眠调度和分布参数Bayes估计值之间的关系式。由此得到了传感器节点在线实时调整的休眠周期调度方案。最后进行了在线调度方案和离线调度方案模拟实验,对相应结果进行了对比分析。实验结果表明相较于离线调度方案,在线调度方案具有更好的适应性。

关键词: 随机事件, 分布参数, Bayes估计, 离线周期调度方案, 在线可调整周期调度方案

Abstract:

Aiming at off-line problem of sensor node sleeping scheduling based on event random property in existing asynchronous sensor networks, Bayesian estimations were given for the distributed parameters of random variables describing random property of random events. The relationship among the instantaneous capture probability, the energy efficiency of the sensor capture event, and the Bayesian estimates was discussed to obtain the relationship between sensor's sleeping scheduling and Bayesian estimation of distributed parameters. Based on the above analysis, the real time adjustment scheduling schemes were obtained for the sensor node sleep. Finally, the study conducted the online scheduling and offline scheduling simulation experiments. Experimental results showed that compared with the offline scheduling scheme, online scheduling scheme had better adaptability.

Key words: random event, distribution parameters, bayesian estimation, off line periodic scheduling scheme, on line adjustable periodic scheduling schemes

中图分类号:

TP393

余志祥,程宗毛,徐向华. 异步传感器网络随机事件捕获在线调度方案[J]. 电子科技, 2019, 32(2): 70-74.

YU Zhixiang,CHENG Zongmao,XU Xianghua. An Online Scheduling Scheme for Random Event Capture in Asynchronous Sensor Networks[J]. Electronic Science and Technology, 2019, 32(2): 70-74.

图/表 5

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参考文献 17

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参数	取值
传感器感知半径/m	1
传感器在工作状态下单位时间内消耗的能量/J	2.369
传感器在休眠状态下单位时间内消耗的能量/J	0.17
传感器工作和休眠状态互相转换一次消耗能量/J	0.05
单位时间/h	1

到达事件数 n /个	指数分布参数θ^-1/h^-1	指数分布参数 β^-1/h-1	泊松分布参数 γ^-1/个
0	1	2	4
200	0.972	0.972	3.891
400	0.964	1.191	3.846
600	0.975	1.340	3.906
800	0.996	1.455	3.937
1 000	0.984	1.550	3.922
1 200	0.991	1.578	3.953

异步传感器网络随机事件捕获在线调度方案

An Online Scheduling Scheme for Random Event Capture in Asynchronous Sensor Networks

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