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Journal of Software:2020.31(12):3852-3866

射频供能传感网面向融合检测的部署调度方法
李燕君,陈雨哲,林瑞仲,池凯凯,胡亚红
(浙江工业大学 计算机科学与技术学院, 浙江 杭州 310023;诺基亚通信系统技术(北京)有限公司浙江分公司, 浙江 杭州 310053)
Deployment and Scheduling for Fusion-based Detection in RF-powered Sensor Networks
LI Yan-Jun,CHEN Yu-Zhe,LIN Rui-Zhong,CHI Kai-Kai,HU Ya-Hong
(School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;Nokia Solutions and Networks System Technology(Beijing) Co., Ltd. Zhejiang Branch, Hangzhou 310053, China)
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Received:October 16, 2018    Revised:June 16, 2019
> 中文摘要: 当射频供能传感网应用于目标检测时,对节点的部署位置和充电/感知调度表进行合理规划可有效提高系统检测质量.基于融合检测模型,首先归纳了使得系统检测质量最大化的节点部署和调度联合优化问题,证明了该问题是NP完全问题.然后分析了融合半径对检测率的影响,提出了基于贪婪算法的节点部署调度联合优化算法.通过小规模网络、大规模网络及基于真实数据集的仿真,将该算法分别与全局最优解、分阶段优化贪婪算法进行了性能比较.实现结果表明:所提出的联合优化贪婪算法获得的系统检测质量在各组仿真中均优于分阶段贪婪算法,并且在小规模网络中接近于全局最优解.
Abstract:When RF-powered sensor network is applied to target detection, rational planning of sensor placement and charging/sensing schedule is an effective way to improve the system detection quality. Based on the fusion-based detection model, firstly, the joint optimization problem of sensor placement and scheduling problem is formulated to maximize the system detection quality. The problem is proved to be NP-complete. Then after analyzing the impact of fusion radius on the detection rate, a joint optimization greedy algorithm (JOGA) is proposed to solve the problem. Finally, the performance of the proposed JOGA is compared with those obtained by exhaustive search and two-stage greedy algorithm (TSGA), an algorithm that optimizes sensor placement and scheduling separately, through extensive numerical simulations as well as simulations based on real data traces collected from a vehicle detection experiment. Results show that, the proposed JOGA always outperforms TSGA in all the simulation scenarios, and is near optimal in small-scale networks.
文章编号:     中图分类号:TP393    文献标志码:
基金项目:国家自然科学基金(61772472,61872322,61472367);浙江省自然科学基金(LZ21F020005);浙江省属高校基本科研业务费专项资金(RF-A2019002);国家重点研发计划(2018YFB0204003) 国家自然科学基金(61772472,61872322,61472367);浙江省自然科学基金(LZ21F020005);浙江省属高校基本科研业务费专项资金(RF-A2019002);国家重点研发计划(2018YFB0204003)
Foundation items:National Natural Science Foundation of China (61772472, 61872322, 61472367); Natural Science Foundation of Zhejiang Province (LZ21F020005); Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-A2019002); National Key R&D Program of China (2018YFB0204003)
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李燕君,陈雨哲,林瑞仲,池凯凯,胡亚红.射频供能传感网面向融合检测的部署调度方法.软件学报,2020,31(12):3852-3866

LI Yan-Jun,CHEN Yu-Zhe,LIN Rui-Zhong,CHI Kai-Kai,HU Ya-Hong.Deployment and Scheduling for Fusion-based Detection in RF-powered Sensor Networks.Journal of Software,2020,31(12):3852-3866