SILIC: Intelligent On/Off Control for Networked Solar Insecticidal Lamps

Heyang Yao; Lei Shu; Yuli Yang; Miguel Martínez-García; Wei Lin

IEEE/CAA Journal of Automatica Sinica

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H. Yao, L. Shu, Y. Yang, M. Martínez-García, and W. Lin, “SILIC: Intelligent on/off control for networked solar insecticidal lamps,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–15, Jan. 2025.

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H. Yao, L. Shu, Y. Yang, M. Martínez-García, and W. Lin, “SILIC: Intelligent on/off control for networked solar insecticidal lamps,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–15, Jan. 2025.

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SILIC: Intelligent On/Off Control for Networked Solar Insecticidal Lamps

Funds: This work was supported in part by the National Natural Science Foundation of China (62072248)

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Author Bio:
Heyang Yao received the M.S. degree in agricultural engineering from Nanjing Agricultural University in 2020. She is currently working toward the Ph.D. degree with the College of Engineering, Nanjing Agricultural University. Her research interests include energy management optimization algorithms, agricultural Internet of Things, and machine learning algorithms

Lei Shu (Senior Member, IEEE) received the B.S. degree in computer science from South Central University for Nationalities in 2002, the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland, in 2010. He is a Professor in Nanjing Agricultural University, and a “Lincoln Professor” of University of Lincoln, UK. He is also the Director of NAU-Lincoln Joint Research Center of Intelligent Engineering. He has published over 500 papers in related conferences, journals, and books in the area of Internet of Things. His H-index is more than 80 and Google Citation (Google Scholar) is more than 25 000 times. and He has been serving as Editor-in-Chief of Journal of Sensor and Actuator Networks, Specialty Chief Editor of Frontiers in Plant Science-Sustainable and Intelligent Phytoprotection Section, and (or was) associate editors for IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Cyber-Physical Systems, IEEE Transactions on Consumer Electronics, IEEE Communications Magazine, IEEE Network Magazine, IEEE Systems Journal, IEEE Access, IEEE/CAA Journal of Automatica Sinica, IEEE Consumer Electronics Magazine, etc. He has served as more than 60 various Co-Chair for international conferences/workshops, e.g., IWCMC, ICC, ISCC, ICNC, Chinacom, IECON, INDIN, ISIE, ICIT, General Co-Chair for Chinacom, Qshine, Collaboratecom, Steering and TPC Chair for InisCom; TPC members of more than 160 conferences, e.g., ICDCS, DCOSS, MASS, ICC, Globecom, ICCCN, WCNC, ISCC, IECON, INDIN, ISIE, ICIT

Yuli Yang (Senior Member, IEEE) received the Ph.D. degree in communications and information systems from Peking University in 2007. She is a Senior Lecturer in communications and networks at the University of Essex, UK. Before joining Essex, she was with the University of Lincoln (UK), the University of Chester (UK), Meliksah University (Turkey), and King Abdullah University of Science and Technology (Saudi Arabia) on various academic positions. Her industry experience includes working as a Research Scientist at Bell Labs Shanghai and an Intern Researcher at Huawei Technologies. Her research interests include modelling, design, analysis, and optimization of wireless systems and networks, specifically in physical-layer security, permutation-based modulation/transmission, and ultrareliable low-latency communications

Miguel Martínez-García (Senior Member, IEEE) received the B.Sc. degree in mathematics from the Polytechnic University of Catalonia (UPC), Spain, in 2013, followed by the M.Sc. in advanced mathematics and mathematical engineering from the same university in 2014. In 2018, he completed the Ph.D. in engineering at the University of Lincoln, UK. Since 2017, he has held various research positions, both at the University of Lincoln and in the Advanced Virtual Reality Research Centre (AVRRC) at Loughborough University. His research pursuits are characterized by an interdisciplinary approach, focusing on the application of artificial intelligence and applied mathematics to engineering. His particular area of expertise includes anomaly detection, intelligent signal processing, topological data analysis, and the analysis of non-linear signals that represent interactions between humans and machines. He has contributed to over 60 publications in the field. He is a Lecturer in Machine Intelligence at the Department of Aeronautical and Automotive Engineering, Loughborough University, UK

Wei Lin (Student Member, IEEE) received the M.S. degree in electronics and communications engineering from Jiangsu University of Science and Technology in 2021. He is currently working toward the Ph.D. degree with the Artificial Intelligence College, Nanjing Agricultural University. His research interest includes machine vision, image processing, and machine learning
Corresponding author: Lei Shu, e-mail: lei.shu@njau.edu.cn
Received Date: 2024-06-10
Accepted Date: 2024-07-17

Available Online: 2024-10-11

Abstract

Abstract

The solar insecticidal lamp (SIL) is an innovative green control device. Nevertheless, a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency. The substantial energy consumption required to turn on the SIL, coupled with the extension of insecticidal working time during the low pest activity periods, can result in low energy efficiency. Especially when the energy storage level is below 50%, the inefficient use of energy significantly reduces the effectiveness of pest control. Consequently, an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization. In this paper, we present the solar insecticidal lamp intelligent energy management scheme (SIL-IEMS) to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things (SIL-IoT). SIL-IEMS primarily utilizes genetic algorithm (GA) and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests. Comparing SIL-IEMS to the traditional remote switching method (TRSM) and the solar insecticidal lamp genetic algorithm (SILGA), our simulation results showcase its superior energy efficiency and pest control effectiveness. Particularly noteworthy is the SILIEMS’s 17.6% increase in insecticidal efficiency compared to TRSM and 6% improvement over SILGA when the SIL begins with a remaining energy level of 15%.

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SILIC: Intelligent On/Off Control for Networked Solar Insecticidal Lamps

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