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.

References

1	黄文江, 师越, 董莹莹, 等. 作物病虫害遥感监测研究进展与展望[J]. 智慧农业, 2019, 1(4): 1-11.
1	HUANG W, SHI Y, DONG Y, et al. Progress and prospects of crop diseases and pests monitoring by remote sensing[J]. Smart Agriculture, 2019, 1(4): 1-11.
2	陈昊楠, 徐翔, 邓晓悦, 等. 单波长杀虫灯对草地贪夜蛾诱杀效果初步评价[J]. 四川农业科技, 2020(2): 41-42.
2	CHEN H, XU X, DENG X, et al. Preliminary evaluation of trapping effect of single long wave insecticidal lamp on Noctuidae in grassland[J]. Sichuan Agricultural Science and Technology, 2020(2): 41-42.
3	王向东. 改进型太阳能杀虫灯对凉山州害虫诱杀效果研究[J]. 西昌学院学报(自然科学版), 2020, 34(1): 9-13， 92.
3	WANG X. Study on effect of improved solar pest-killing lamp on trapping and killing field pests in liangshan[J]. Journal of Xichang University(Natural Science Edition), 2020, 34(1): 9-13， 92.
4	孙康, 王静秋, 冷晟, 等. 基于物联网的温室环境监控系统[J]. 测控技术, 2019, 38(9): 118-121.
4	SUN K, WANG J, LENG S, et al. Monitoring and management system of greenhouse based on IoT[J]. Measurement & Control Technology, 2019, 38(9): 118-121.
5	黄蕊, 郭树强, 赵红茹. 基于无线传感网络的无人机喷药沉积效果检测[J]. 农机化研究, 2019, 41(5): 206-209.
5	HUANG R, GUO S, ZHAO H. Detection of UAV spray deposit effect based on wireless sensor network[J]. Journal of Agricultural Mechanization Research, 2019, 41(5): 206-209.
6	徐凌翔, 陈佳玮, 丁国辉, 等. 室内植物表型平台及性状鉴定研究进展和展望[J]. 智慧农业, 2020, 2(1): 23-42.
6	XU L, CHEN J, DING G, et al. Indoor phenotyping platforms and associated trait measurement: Progress and prospects[J]. Smart Agriculture, 2020, 2(1): 23-42.
7	李凯亮, 舒磊, 黄凯, 等. 太阳能杀虫灯物联网研究现状与展望[J]. 智慧农业, 2019, 1(3): 13-28.
7	LI K, SHU L, HUANG K, et al. Research and prospect of solar insecticidal lamps Internet of Things[J]. Smart Agriculture, 2019, 1(3): 13-28.
8	张磊. 杀虫灯主要技术的发展分析[J]. 四川农业农机, 2016(4): 32-33.
8	ZHANG L. Development analysis of main technology of insecticidal lamp[J]. Sichuan Agriculture and Agricultural Machinery, 2016(4): 32-33.
9	LAM B H, PHAN T T, VUONG L H, et al. Designing a brown planthoppers surveillance network based on wireless sensor network approach[J]. Computer Science, 2013.
10	ELIOPOULOS P A, POTAMITIS I, KONTODIMAS D C. Estimation of population density of stored grain pests via bioacoustic detection[J]. Crop Protection, 2016, 85: 71-78.
11	LOPEZ O, RACH M O, MIGALLON H, et al. Monitoring pest insect traps by means of low-power image sensor technologies[J]. Sensors, 2012, 12(11): 15801-15819.
12	YANG F, SHU L, HUANG K, et al. A partition-based node deployment strategy in solar insecticide lamp internet of things[J]. IEEE Internet of Things Journal, 2020. doi: 10.1109/JIOT.2020.2996514.
13	CHOUIKHI S, KORBI I E, GHAMRIDOUDANE Y, et al. A survey on fault tolerance in small and large scale wireless sensor networks[J]. Computer Communications, 2015, 69(69): 22-37.
14	MORIDI E, HAGHPARAST M, HOSSEINZADEH M, et al. Fault management frameworks in wireless sensor networks: A survey[J]. Computer Communications, 2020, 155: 205-226.
15	SWAIN R R, DASH T, KHILAR P M. A complete diagnosis of faulty sensor modules in a wireless sensor network[J]. Ad Hoc Networks, 2019, 93: 101924-101944.
16	CHESSA S, SANTI P. Crash faults identification in wireless sensor networks[J]. Computer Communications, 2002, 25(14): 1273-1282.
17	PANDA M, KHILAR P M. Distributed soft fault detection algorithm in wireless sensor networks using statistical test[C]// IEEE International Conference on Parallel Distributed & Grid Computing. Piscataway, New York, USA: IEEE, 2012.
18	ELSAYED W, ELHOSENY M, SABBEH S, et al. Self-maintenance model for wireless sensor networks[J]. Computers and Electrical Engineering, 2017, 7: 99-812.
19	HE W, QIAO P, ZHOU Z, et al. A new belief-rule-based method for fault diagnosis of wireless sensor network[J]. IEEE Access, 2018, 6: 9404-9419.
20	PARADIS L, HAN Q. A survey of fault management in wireless sensor networks[J]. Journal of Network and Systems Management, 2007, 15(2): 171-190.
21	YU M, MOKHTAR H, MERABTI M. A survey on fault management in wireless sensor networks[J]. IEEE Wireless Communications, 2008, 14(6): 13-19.
22	李昌炽, 王志勇. 佳多牌频振式杀虫灯的使用注意事项与维修方法[J]. 植物医生, 2012, 25(6): 51-52.
22	LI C, WANG Z. Attention and maintenance of Jiaduo Frequency Vibration insecticidal lamp[J]. Plant Doctor, 2012, 25(6): 51-52.
23	宋文海, 李田泽, 乔家振, 等. TCT结构光伏阵列故障检测方法研究[J]. 电源技术, 2019, 43(7): 1164-1167.
23	SONG W, LI T, QIAO J, et al. Research on fault detection method of TCT structured photovoltaic array[J]. Chinese Journal of Power Sources, 2019, 43(7): 1164-1167.
24	BAE J, LEE M, SHIN C. A data-based fault-detection model for wireless sensor networks[J]. Sustainability, 2019, 11(21): ID 6171.
25	SI S, WANG J, YU C, et al. Energy-efficient and fault-tolerant evolution models based on link prediction for large-scale wireless sensor networks[J]. IEEE Access, 2018, 6: 73341-73356.
26	JIANG P. A new method for node fault detection in wireless sensor networks[J]. Sensors, 2009, 9(2): 1282-1294.
27	LAU B C P, MA E W M, CHOW T W S. Probabilistic fault detector for Wireless Sensor Network[J]. Expert Systems With Applications, 2014, 41(8): 3703-3711.
28	PANDA M, KHILAR P M. Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test[J]. Ad Hoc Networks, 2015, 25: 170-184.
29	JIN X, CHOW T W S, SUN Y, et al. Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis[J]. Wireless Networks, 2015, 21(3): 829-839.
30	PENG T, CHOW T W S. Wireless sensor-networks conditions monitoring and fault diagnosis using neighborhood hidden conditional random field[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3): 933-940.
31	O?NER C, BUCHMANN E, B?HM K. Identifying defective nodes in wireless sensor networks[J]. Distributed and Parallel Databases, 2016, 34(4): 591-610.
32	CHANAK P, BANERJEE I, SHERRATT R S. Mobile sink based fault diagnosis scheme for wireless sensor networks[J]. Journal of Systems and Software, 2016, 119: 45-57.
33	SULIEMAN N I, GITLIN R D. Ultra-reliable and energy efficient wireless sensor networks[C]// Wireless and Microwave Technology Conference. Piscataway, New York, USA: IEEE, 2018.
34	JASSBI S J, MORIDI E. Fault tolerance and energy efficient clustering algorithm in wireless sensor networks: FTEC[J]. Wireless Personal Communications, 2019, 107(1): 373-391.
35	LIU L, HAN G, HE Y, et al. Fault-tolerant event region detection on trajectory pattern extraction for industrial wireless sensor networks[J]. IEEE Transactions on Industrial Informatics, 2020, 16(3): 2072-2080.
36	ZHAO M, TIAN Z, CHOW T W S. Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation[J]. Neural Computing and Applications, 2019, 31(8): 4019-4030.
37	JAVAID A, JAVAID N, WADUD Z, et al. Machine learning algorithms and fault detection for improved belief function based decision fusion in wireless sensor networks[J]. Sensors， 2019, 19(6): ID 1334.
38	FISSAOUI M E, BENI-HSSANE A, SAADI M. Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(2): 569-578.
39	MORIDI E, HAGHPARAST M, HOSSEINZADEH M, et al. Novel fault-tolerant clustering-based multipath algorithm (FTCM) for wireless sensor networks[J]. Telecommunication Systems, 2020, 174: 411-424.
40	HEINZELMAN W R. Energy-efficient communication protocol for wireless microsensor networks[C]// IEEE Computer Society. Piscataway, New York, USA: IEEE, 2000.
41	LIN J, CHELLIAH P R, HSU M, et al. Efficient fault-tolerant routing in IoT wireless sensor networks based on bipartite-flow graph modeling[J]. IEEE Access, 2019, 7: 14022-14034.
42	SWAIN R R, KHILAR P M, DASH T. Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance[J]. International Journal of Communication Systems, 2018, 31(14): 3769-3786.
43	RODRIGUES A, CAMILO T, SILVA J S, et al. Diagnostic tools for wireless sensor networks: A comparative survey[J]. Journal of Network and Systems Management, 2013, 21(3): 408-452.
44	RAMANATHAN N, CHANG K, KAPUR R, et al. Sympathy for the sensor network debugger[C]// International Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2005.
45	YANG J, SOFFA M L, SELAVO L, et al. Clairvoyant: A comprehensive source-level debugger for wireless sensor networks[C]// International Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2007.
46	RINGWALD M, ROMER K, VITALETTI A, et al. Passive inspection of sensor networks[C]// Distributed Computing in Sensor Systems. Heidelberg, Berlin, Germany: Springer, 2007.
47	KHAN M M, LE H K, AHMADI H, et al. Dustminer: Troubleshooting interactive complexity bugs in sensor networks[C]// International Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2008.
48	COLIN A, HARVEY G, LUCIA B, et al. An Energy-interference-free hardware-software debugger for intermittent energy-harvesting systems[C]// Architectural Support for Programming Languages and Operating Systems. New York, USA: ACM, 2016.
49	YIN Z, LI F, SHEN M, et al. Fault-tolerant topology for energy-harvesting heterogeneous wireless sensor networks[C]// International Conference on Communications. Piscataway, New York: IEEE, 2015.
50	ZHANG X, YAO G, DING Y, et al. An improved immune system-inspired routing recovery scheme for energy harvesting wireless sensor networks[J]. Soft Computing, 2017, 21(20): 5893-5904.
51	video “Demo.” High voltage discharge exhibits severe effect on ZigBee-based device in solar insecticidal lamps Internet of Things[EB/OL]. (2020-01-13) [2020-06-08]. .

[1]	HUANG Kai, SHU Lei, LI Kailiang, YANG Xing, ZHU Yan, WANG Xiaochan, SU Qin. Design and Prospect for Anti-theft and Anti-destruction of Nodes in Solar Insecticidal Lamps Internet of Things [J]. Smart Agriculture, 2021, 3(1): 129-143.
[2]	SUN Haoran, SUN Lin, BI Chunguang, YU Helong. Hybrid Multi-Hop Routing Algorithm for Farmland IoT based on Particle Swarm and Simulated Annealing Collaborative Optimization Method [J]. Smart Agriculture, 2020, 2(3): 98-107.
[3]	YANG XuanJiang, LI Hualong, LI Miao, HU Zelin, LIAO Jianjun, LIU Xianwang, GUO Panpan, YUE Xudong. Beehive Key Parameters Online Monitoring System and Performance Test [J]. Smart Agriculture, 2020, 2(2): 115-125.
[4]	Li Kailiang, Shu Lei, Huang Kai, Sun Yuanhao, Yang Fan, Zhang Yu, Huo Zhiqiang, Wang Yanfei, Wang Xinyi, Lu Qiaoling, Zhang Yacheng. Research and prospect of solar insecticidal lamps Internet of Things [J]. Smart Agriculture, 2019, 1(3): 13-28.
[5]	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.
[6]	Zhao Chunjiang. State-of-the-art and recommended developmental strategic objectivs of smart agriculture [J]. Smart Agriculture, 2019, 1(1): 1-7.