面向大规模农田生境监测的无线传感器网络节能优化策略

doi:10.12133/j.smartag.2019.1.2.201812-SA024

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (2): 55-63.doi: 10.12133/j.smartag.2019.1.2.201812-SA024

面向大规模农田生境监测的无线传感器网络节能优化策略

张晓涵¹, 尹长川^1,^*(), 吴华瑞²

^1. 北京邮电大学网络体系构建与融合北京市重点实验室,北京 100876
^2. 北京市农林科学院农业信息技术研究中心,北京 100097

收稿日期:2018-12-20 修回日期:2019-04-15 出版日期:2019-04-30
基金资助:
国家自然科学基金(61871041);国家自然科学基金(61629101);北京市自然科学基金-市教委联合资助项目(KZ201911232046)
作者简介:张晓涵（1993-）,女,硕士,研究方向：农业物联网,Email: zxh2011@bupt.edu.cn。
通信作者:

Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring

Zhang Xiaohan¹, Yin Changchuan^1,^*(), Wu Huarui²

^1. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
^2. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, 100079, China

Received:2018-12-20 Revised:2019-04-15 Online:2019-04-30

摘要/Abstract

摘要：

针对大规模农田生境监测场景中无线传感器网络节点在部分作物生长期内呈现节点空间冗余,以及传感器节点采集到的数据之间通常具有很强的时间关联性的特点,本研究提出一种基于矩阵补全的两步节能优化策略来同时降低传感器网络的数据采集和传输能耗,以实现延长网络寿命的目的。该算法首先通过对节点数据信息量的衡量来寻找出空间上的非冗余节点,剩余的冗余节点关闭其采集功能,只作为中继节点传输数据;其次,利用矩阵补全算法的部分采样原理在采样阶段进一步减少时间上的数据冗余量,达到同时降低采集和传输模块能耗的目的。试验结果表明,所提出的算法可减少网络中83%的工作节点数目,有效降低了网络能耗。

Abstract:

Wireless sensor network (WSN) has been widely deployed in precision agriculture to improve the crop production. However, we still face many challenges for large-scale farmland habitat monitoring, the energy shortage for battery-powered sensor nodes, the complicated propagation environment for wireless signals during different growing-up periods of the crops, the optimization of the coverage of the sensor nodes, etc. In order to guarantee the holeless coverage of the sensor nodes during the whole life of the crops, the WSNs are typically deployed with high density. Therefore, some of the sensors in the network are redundant in certain growing-up periods of the crops. And also the data collected by each sensor node may have strong temporal correlation. Recently, compressive sensing (CS) has received much attention due to its capability of reconstructing sparse signals with the number of measurements much lower than that of the Nyquist sampling rate. With the rapid progress of CS based sparse representation, matrix completion (MC) theory was proposed very recently. According to the MC theory, a low-rank matrix can be accurately rebuilt with a few number of entries in the matrix. Matrix completion provides the advantage of sampling small set of data at sensor nodes without requiring excessive computational and traffic loads, which meets the requirement of energy-efficient data gathering and transmitting in WSNs. In this research, by considering the characteristics that the sensor nodes are redundant in some growing-up periods of the crops and the data collected by sensor-nodes usually share a strong spatial and temporal correlation among them in WSNs for large-scale farmland habitat monitoring, we put forward a MC based two-step energy saving optimization algorithm to reduce both the energy consumption of the data acquisition and data transmission process in WSNs and achieved the purpose of prolonging the network lifetime. Firstly, through the measurement of the sensor node's data information, we found the non-redundant nodes by considering the spatial correlation of the data from the sensor nodes. We would close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes. Secondly, we took advantage of the partial sampling scheme in matrix completion to further reduce the quantity of data. Thereby, we could reduce the energy consumption on both data collection and transmission process of the wireless sensor network. The experiment results show that the proposed algorithm reduces 83% working nodes in the network, and therefore reducing the energy cost of the network.

Key words: wireless sensor networks, temporal and spatial correlations, matrix completion, large-scale farmland, habitat monitoring

中图分类号:

张晓涵, 尹长川, 吴华瑞. 面向大规模农田生境监测的无线传感器网络节能优化策略[J]. 智慧农业, 2019, 1(2): 55-63.

Zhang Xiaohan, Yin Changchuan, Wu Huarui. Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring[J]. Smart Agriculture, 2019, 1(2): 55-63.

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面向大规模农田生境监测的无线传感器网络节能优化策略

Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring

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