| 软件定义无线传感器网络中低控制负载的睡眠调度 |
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| 引用本文： | 赵 腾1,王海晖1,2 ,彭 云1,赵茂阳1,张水平1,2 . 软件定义无线传感器网络中低控制负载的睡眠调度[J]. 武汉工程大学学报, 2017, 39(5): 488-495 |
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| 作者姓名： | 赵 腾1 王海晖1 2 彭 云1 赵茂阳1 张水平1 2 |
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| 作者单位： | 1. 武汉工程大学计算机科学与工程学院，湖北 武汉 430205； 2. 智能机器人湖北省重点实验室（武汉工程大学），湖北 武汉 430205 |
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| 摘 要： | 为了克服软件定义无线传感器网络（SDWSN）中控制流量的限制，依据基于能量消耗的连通k邻域睡眠调度算法和软件定义网络（SDN）的特性，提出了一种低控制负载的睡眠调度方案. 首先，利用SDN的网络模型重新设计无线传感器网络的模型；然后，依据新的网络模型，对传统的睡眠调度方案进行改进. 在此基础上，设计出能够降低网络中控制流量的流表，从而降低SDWSN中的控制负载. 仿真结果表明，本文所提出的控制流设计方案在控制流量和数据平面中更新节点状态的平均响应时间要优于SDWSN中的睡眠调度，该方案能够最小化整个网络中的控制流量，在延长网络生命周期的同时降低控制负载.
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| 关 键 词： | 无线传感器网络 软件定义网络 睡眠调度 |
Low Control Overhead-Based Sleep Scheduling for Software-Befined Wireless Sensor Networks |
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| ZHAO Teng1,WANG Haihui1,' target="_blank" rel="external">2,PENG Yun1,ZHAO Maoyang1,ZHANG Shuiping1,' target="_blank" rel="external">2. Low Control Overhead-Based Sleep Scheduling for Software-Befined Wireless Sensor Networks[J]. Journal of Wuhan Institute of Chemical Technology, 2017, 39(5): 488-495 |
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| Authors: | ZHAO Teng1,WANG Haihui1,' target=" _blank" rel=" external" >2,PENG Yun1,ZHAO Maoyang1,ZHANG Shuiping1,' target=" _blank" rel=" external" >2 |
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| Affiliation: | 1. School of Computer Science and Technology， Wuhan Institute of Technology， Wuhan 430205， China 2. Hubei Key Laboratory of Intelligent Robot（Wuhan Institute of Technology）， Wuhan 430205， China |
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| Abstract: | To overcome the limitation of the control flow in the software-defined wireless sensor network （SDWSN）， a low control overhead sleep scheduling program was proposed based on the energy consumption of the connected k neighborhood sleep scheduling algorithm and the characteristics of software-defined network （SDN）. Firstly， the model of the wireless sensor network was redesigned by using the SDN network model. Then， the traditional sleep scheduling scheme was improved according to the new network model. On the basis， the flow table can be designed to reduce the size of the control flow in the network and the control flow overhead can be reduced in the SDWSN. The simulation results show that the control flow size and the average response time of updating the node status in the data plane are better than those in the SDWSN. The proposed scheme can minimize the total control flow size in the whole network and reduce the control overhead while extending the network life cycle. |
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| Keywords: | wireless sensor network software-defined networking sleep scheduling |
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