Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things

doi:10.12133/j.smartag.2020.2.2.202005-SA002

Abstract

Abstract:

Solar insecticidal lamps Internet of Things (SIL-IoTs) is a novel physical agricultural pest control implement, which is an emerging paradigm that extends Internet of Things technology towards Solar Insecticidal Lamp (SIL). SIL-IoTs is composed of SIL nodes with functions of preventing and controlling of agricultural migratory pests with phototaxis feature, which can be deployed over a vast region for the purpose of ensuring pests outbreak area location, reducing pesticide dosage and monitoring agricultural environmental conditions. SIL-IoTs is widely used in agricultural production, and a number of studies have been conducted. However, in most current research projects, fault diagnosis has not been taken into consideration, despite the fact that SIL-IoTs faults have an adverse influence on the development and application of SIL-IoTs. Based on this background, this research aims to analyze the characteristics and challenges of fault diagnosis in SIL-IoTs, which naturally leads to a great number of open research issues outlined afterward. Firstly, an overview and state-of-art of SIL-IoTs were introduced, and the importance of fault diagnosis in SIL-IoTs was analyzed. Secondly, faults of SIL nodes were listed and classified into different types of Wireless Sensor Networks (WSNs) faults. Furthermore, WSNs faults were classified into behavior-based, time-based, component-based, and area affected-based faults. Different types of fault diagnosis algorithms (i.e., statistic method, probability method, hierarchical routing method, machine learning method, topology control method, and mobile sink method) in WSNs were discussed and summarized. Moreover, WSNs fault diagnosis strategies were classified into behavior-based strategies (i.e., active type and positive type), monitoring-based strategies (i.e., continuous type, periodic type, direct type, and indirect type) and facility-based strategies (i.e., centralized type, distributed type and hybrid type). Based on above algorithms and strategies, four kinds of fault phenomena: 1) abnormal background data, 2) abnormal communication of some nodes, 3) abnormal communication of the whole SIL-IoTs, and 4) normal performance with abnormal behavior actually were introduced, and fault diagnosis tools (i.e., Sympathy, Clairvoyant, SNIF and Dustminer) which were adapted to the mentioned fault phenomena were analyzed. Finally, four challenges of fault diagnosis in SIL-IoTs were highlighted, i.e., 1) the complex deployment environment of SIL nodes, leading to the fault diagnosis challenges of heterogeneous WSNs under the condition of unequal energy harvesting, 2) SIL nodes task conflict, resulting from the interference of high voltage discharge, 3) signal loss of continuous area nodes, resulting in the regional link fault, and 4) multiple failure situations of fault diagnosis. To sum up, fault diagnosis plays a vital role in ensuring the reliability, real-time data transmission, and insecticidal efficiency of SIL-IoTs. This work can also be extended for various types of smart agriculture applications and provide fault diagnosis references.

Key words: solar insecticidal lamp, Wireless Sensor Networks, agricultural Internet of Things, fault diagnosis, insect disaster

CLC Number:

S237

YANG Xing, SHU Lei, HUANG Kai, LI Kailiang, HUO Zhiqiang, WANG Yanfei, WANG Xinyi, LU Qiaoling, ZHANG Yacheng. Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things[J]. Smart Agriculture, 2020, 2(2): 11-27.

Figures/Tables 9

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	故障	原因	现象	厂家诊断策略	在WSNs中表现的特征
太阳能板	1.母线断线 2.热斑	1.布线不良 2.部分电池被遮盖	减少电池寿命，降低输出功率	监测输入电压电流	充电能力下降，能量较低时易出现节点与网络故障
蓄电池	1.欠压 2.硫化 3.不进电	1.老化 2.大电流放电 3.线路松动或整流器故障	启动性能与充电性能下降，无法充满电量，无法正常放电	监测充满后的电量，充电时长，输出电流与电压值	电池损坏导致永久性硬故障，无法充满电量导致网络寿命降低
高压电网	无电	断路或变压器损坏，虫体或残留污垢过多	吸引害虫至杀虫灯附近却无法杀虫，导致杀虫灯附近害虫密集	监测杀虫计数情况，电压情况	不放电导致此节点与其他节点相比能量消耗异常
集成电路	1.氧化受雨水侵蚀	1.密封性不强 2.防水措施不强	电路失效，导致部分或所有组件无法正常使用	通过其他组件故障判断电路故障	部分或全部组件失效导致WSNs出现硬故障、节点故障、网络故障
灯管	1.灯管频繁启动 2.灯管不亮 3.灯管两端发红，但不起跳	1.灯管老化或电压低 2.断路 3.起跳器故障	无法吸引害虫至杀虫灯附近	监测杀虫计数情况，电压情况	不吸引害虫导致此节点与其他节点相比能量消耗异常
风扇	停转或转速异常	电机老化，电压不足，吸入异物	电机损坏，害虫吸引至杀虫灯附近却无法杀虫	监测电压电流情况	停转导致此节点与其他节点相比能量消耗异常

作者	年份	基于行为		基于时间			基于组件				基于区域		方法
作者	年份	硬	软	永久	暂时	间歇	节点	网络	基站	后端	局部	全局	主动	被动	类型
Jiang^[26]	2009		√		√	√	√					√	√		统计方法
Lau等^[27]	2014	√		√	√	√		√				√	√		概率方法
Panda 和Khilar^[28]	2015	√	√	√			√				√		√		统计方法
Jin等^[29]	2015		√		√	√	√	√				√		√	统计方法
Peng和Chow^[30]	2016		√	√	√	√		√			√		√		概率方法
O?ner等^[31]	2016	√	√		√	√		√				√		√	拓扑控制方法
Chanak等^[32]	2016	√	√	√	√	√	√	√				√	√		移动基站方法
Sulieman和Gitlin^[33]	2018	√		√				√			√		√		拓扑控制方法
Jassbi和Moridi^[34]	2019		√		√	√	√	√			√		√		层次路由方法
Liu等^[35]	2019		√		√	√	√	√				√	√		概率方法
Zhao等^[36]	2019		√		√	√	√	√				√	√		机器学习方法
Javaid等^[37]	2019	√	√	√	√	√	√					√	√		机器学习方法
Fissaoui等^[38]	2019	√	√	√	√	√	√	√	√			√	√		移动基站方法
Moridi等^[39]	2020		√	√	√	√	√					√		√	层次路由方法

故障现象	可能诱因	建议方法	建议策略
后台数据异常	1.节点软故障 2.基站或后台故障 3.灯管、高压电网或风扇故障	统计方法、概率方法、机器学习方法、移动基站方法	通过正常数据的历史经验主动定期诊断与被动连续监测，若节点长时间未能修复，则需维护人员现场检查
部分节点通信异常	1.节点处于相同链路且相邻时，可能是关键节点硬故障导致整条链路网络故障 2.否则为节点硬故障或区域边缘区域的节点网络故障 3.集成电路、太阳能板或蓄电池故障	层次路由方法、拓扑控制方法	通过网络时延等参数定期间接诊断或节点端诊断，排查故障节点或链路，若节点长时间未能修复，则需维护人员现场检查
整个网络通信异常	1.黑客攻击 2.基站故障 3.连续极端天气导致所有节点电量耗尽	安全机制、移动基站方法、特殊情况下杀虫任务与数据传输任务优先级调整	定期诊断或被动诊断，提高WSNs安全机制，SIL杀虫与数据传输优先级分配，无法修复时需维护人员现场检查
未诊断出异常但实际存在异常	1.存在恶意节点导致拜占庭故障 2.故障诊断方法不完善 3.后台故障	故障容错方法、安全机制、综合故障诊断方法	提高WSNs故障容错与安全机制，完善故障诊断方法

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