Latest Research Results

Recently updated on: 2024-04-07

Rethinking Sustainable Sensing in Agricultural Internet of Things: From Power Supply Perspective

Agricultural Internet of Things (IoT) is expected to address several challenges facing the current agriculture industry, including food production, food safety, ecological environment protection, and food waste. However, before achieving this blueprint, a fundamental problem that should be addressed is the sustainability. Unfortunately, solar energy harvesting, which is today’s common approach for sustainable agricultural IoT, has many limitations and can barely support future development. This article bridges this gap by proposing a versatile power supply paradigm, called PowerEdge, that organically integrates ambient energy harvesting, distributed energy storage, wireless power transfer, and intelligent reflecting surface techniques to achieve sustainable smart agricultural operations. A proof of concept with commercially available products is also presented, along with extensive experimental studies in five scenarios. Several interesting novel observations are found. Finally, four technical challenges and four open research issues associated with the proposed solution are discussed.

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[1] Ye Liu, Dong Li, Bangsong Du, Lei Shu and Guangjie Han, “Rethinking Sustainable Sensing in Agricultural Internet of Things: From Power Supply Perspective,” in IEEE Wireless Communications, vol. 29, no. 4, pp. 102-109, August 2022, doi: 10.1109/MWC.004.2100426.

Pests Phototactic Rhythm Driven Solar Insecticidal Lamp Device Evolution: Mathematical Model Preliminary Result and Future Directions

This study presents for the first time the Solar Insecticidal Lamp versions 1.0 to 5.0, pioneering the mathematical model of pest phototactic rhythm based on four major pests and integrating this model with the solar insecticidal lamp. The research accurately predicts the nocturnal activity peaks of pests, providing a solid scientific basis for optimizing the operation times of devices such as solar insecticidal lamps. This innovative model not only significantly enhances the energy efficiency of solar insecticidal lamps but also marks a major advancement in the field of agricultural electronics, contributing essential technical support for the intelligent and sustainable development of agriculture. Additionally, the study explores the differences between static mathematical models and dynamic artificial intelligence (AI) models and looks ahead to the potential of applying dynamic AI models in solar insecticidal lamp. This forward-thinking consideration opens a new chapter in agricultural pest management technology, heralding innovative developments in intelligent pest management and introducing new research directions and application prospects in the field of agricultural technology.

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[1] Heyang Yao, Lei Shu, Wei Lin, Kai Huang, Miguel Martínez-García, Xiuguo Zou, “Pests Phototactic Rhythm Driven Solar Insecticidal Lamp Device Evolution: Mathematical Model Preliminary Result and Future Directions,” IEEE Open Journal of the Industrial Electronics Society, 2024

A Trajectory-Inspired Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things under Coverage and Maintenance Cost Considerations

As a special type of node, Solar Insecticidal Lamps (SILs) require regular maintenance to ensure effective insecticidal performance and accurate collection of pest information. While hiring professionals for management and maintenance is a viable solution, it comes with the drawback of high maintenance costs. An effective approach to reducing these costs is deploying SILs along routes frequently traversed by Agricultural Workers (AWs), as these tasks can be easily incorporated into their routine. Therefore, inspired by the trajectory of high-density AWs, this paper focuses on studying the constrained SIL Deployment Problem under Coverage and Maintenance Cost considerations. In this problem, SIL nodes are deployed at a limited set of weighted Candidate Locations (CLs) situated on the ridges. The objective of cSILDP-CMC is to select a subset of CLs for SIL placement, maximizing coverage while keeping the total maintenance cost within the allocated budget.

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[1] Fan. Yang, Lei. Shu, “A Trajectory-Inspired Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things under Coverage and Maintenance Cost Considerations,” in IEEE Transaction on AgriFood Electronic. DOI: 10.1109/TAFE.2024.3349566

Security and Privacy in Solar Insecticidal Lamps Internet of Things: Requirements and Challenges

In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability, and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

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[1] Qingsong Zhao, Lei Shu*, Kailiang Li, Mohamed Amine Ferrag, Ximeng Liu, and Yanbin Li.  Security and Privacy in Solar Insecticidal Lamps Internet of Things: Requirements and Challenges[J].  IEEE/CAA Journal of Automatica Sinica. 2023. doi: 10.1109/JAS.2023.123870

A Novel Accurate Insecticidal Counting Method Based on Solar Insecticidal Lamp using Machine Learning

The contributions of this study are as follows:

1) The testbed about SIL is modified with special design by changing high voltage package and adding sound sensor module as well as voltage comparator module, which can meet the requirements of both key parameter acquisition and low cost;

2) The relationship between insecticidal counting and Pulse Number of Insecticidal Discharges (PNID) as well as Pulse Number of Insecticidal Sounds (PNIS) is investigated and exhibited a significant positive correlation;

3) The machine learning (ML) algorithm is introduced into this application with an accuracy of 0.85, which is greatly beneficial to the popularization and application of this counting technology.

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Fig. 1. Inspiration from the sound wave diagram in (c) of the two different insecticidal phenomena in (a) and (b).

[1] Kai Huang, Lei Shu, Kailiang Li, Yuyu Feng, Xing Yang, Ye Liu, Fan Yang, and Yan Zhu. A Novel Accurate Insecticidal Counting Method Based on Solar Insecticidal Lamp using Machine Learning[J].IEEE Transactions on Consumer Electronics, 2023.

A Scheme for Pest-Dense Area Localization with Solar Insecticidal Lamps Internet of Things under Asymmetric Links

Propose an algorithm named as PALS for the pest-dense area localization under asymmetric links with SIL-IoTs large-deployment scenarios. The cross-detection and the elimination of pest-dense area errors are introduced in detail. Accurately locating pest-dense areas under asymmetric links provides further theoretical support for accurate pest control in agricultural production scenarios.

Fig 1. Diagram of the PALS strategy for locating pest-dense areas. (a) find the inner and outer boundary nodes and determine the critical edge. (b) take the critical edge as the starting position, and explore for the next edge according to the left-hand rule. (c) get a boundary face until reaching the starting critical edge AB. (d) start the second round of exploration with the backup node information. (e) after two round of exploration is completed, a boundary of a pest-dense area is obtained.

[1] Yuan Li†, Bangsong Du†, Lin Luo, Yusheng Luo, Xing Yang, Ye Liu ,Lei Shu*,A Scheme for Pest-Dense Area Localization with Solar Insecticidal Lamps Internet of Things under Asymmetric Links, IEEE Transactions on AgriFood Electronics, 2023.

The phototactic rhythm of pests for the Solar Insecticidal Lamp: A review

1) We present background knowledge on pest phototactic rhythm from the pest management perspective and the current state of research on pest phototactic rhythm as summarised in our work. The application and benefits of pest phototropic rhythms in pest management are discussed in the pest’s characteristics and the smart agriculture challenge issues.
2) To deeply understand the phototactic rhythm of pests in different crops, a comprehensive research outline on the phototactic rhythm of pests is summarized. Among them, crops are divided into two main varieties: food crops (e.g., rice, soybean, and maize) and economic crops (e.g., cotton, vegetable, orchards, tea).
3) The key factors of pest phototropic rhythms are analyzed to provide data support for the agricultural planters. The key factors include the following four main aspects: meteorological conditions (e.g., temperature, humidity, precipitation, light intensity), insecticidal devices(e.g., wavelength and height of insecticidal devices), physiological states (e.g., sex, age, and dark adaptation), and others(e.g., crop varieties and natural enemies).
4) Finally, some research challenges about the pests phototactic rhythm are discussed.

Fig. 1  The related work on phototactic rhythm of pests

[1] Heyang Yao, Lei Shu, Fan Yang, Yinghao Jin, Yuli Yang. “The phototactic rhythm of pests for the Solar Insecticidal Lamp: A review.” Frontiers in Plant Science, 13 (2023): 5532. doi=10.3389/fpls.2022.1018711

Image dataset of tea chrysanthemums in complex outdoor scenes

The Scientific Value and Potential Applications of the Dataset: Due to the different flowering periods of tea chrysanthemums, it is quite difficult to obtain outdoor tea chrysanthemum images, resulting in insufficient data and insufficient training accuracy. At the same time, considering the changes in lighting, occlusion, and object scale, establishing a relevant chrysanthemum detection model for tea chrysanthemums in complex outdoor environments is a challenge. In this context, providing relevant data for researchers to further study the problems of large individual differences in tea chrysanthemums, complex environments, easy occlusion, and the difficulty in detecting chrysanthemum flowers, I need to provide relevant data to enable researchers to further investigate these issues. This will facilitate the acceleration of practical applications of deep learning algorithms in agriculture.

Fig. 1. The category to which the sample images belong and their number in the related chrysanthemum dataset: (A) Jinsihuangju-01314, (B) Hangbaiju-00063, (C) Bo-chrysanthemum-11093, (D) Wuyuanhuangju-03285, (E) Gongju-00002, and (F) Chuju-0011.

Fig. 2 (A) TES-1333R Solar Power Meter, (B) Mi 10 phone, and (C) Image acquisition scene, (D) Schematic diagram of the camera angles.

[1] Siyang Zang, Lei Shu, Kai Huang, Zhiyong Guan, Ru Han, Ravi Valluru, Xiaochan Wang, Jiaxu Bao, Ye Zheng, Yifan Chen. Image Dataset of Tea Chrysanthemums in Complex Outdoor Scenes. Frontiers in Plant Science, 2023, 14. DOI=10.3389/fpls.2023.1134911.

Insecticidal counting dataset based on one solar insecticidal lamp and two cameras

  • Establish the relevant insecticidal counting model to accurately describe the insecticidal quantity by machine learning, and further describe the pests population density in the area where SIL are deployed. After finishing the insecticidal counting, the insecticidal quantity at different times can further facilitate the research on SIL energy management, including the time and duration of turning on and off the lamp.

Fig. 1 LM393 voltage comparator module (A), Printed Circuit Board (PCB) (B), Risym sound sensor module (C), and insecticidal lamp monitored by two cameras (D) are the major components of the testbed (E).

[1] Kai Huang, Lei Shu, Kailiang Li, Yuyu Feng, Zitian Jiang, Yan Zhu. Insecticidal Counting Dataset Based on One Solar Insecticidal Lamp and Two Cameras. Frontiers in Plant Science, 2022, 13. DOI=10.3389/fpls.2022.995118

SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoTs with Improved Energy Efficiency

  • To the best of our knowledge, this is the first study that addresses the above issues (i.e., fault description and lightweight fault self-detection according to existing modules of SIL-IoTs nodes) by a systematic fault self-detection scheme (SILOS), including hardware design and a fault self-detection model.
  • We developed an improved SIL-IoTs node with enhanced functionality to measure faulty conditions and reduce faults of SIL-IoTs.
  • Based on the fault dictionary concept, faults of SIL-IoTs nodes are summarized and labeled systematically for the first time. Due to the limited energy and large-scale deployment of SIL-IoTs nodes, a concise report rather than all measured data is more suitable for fault detection. Therefore, faults of SIL-IoTs nodes are coded as one byte to reduce delay, energy consumption, and network traffic.
  • An energy/resource efficient implementation of our proposed binary-based sliding window fault self-detection algorithm is performed on the microcontroller (e.g., limited data storage and computing capacities, including 20MHz CPU speed, 32KB program memory size, and 2KB RAM size).

[1] Xing Yang, Lei Shu*, Kailiang Li, Zhiqiang Huo, Sheng Shu, Edmond Nurellari , “SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoTs with Improved Energy Efficiency”, IEEE Internet of Things Journal, 2022.

A Partition-based Mobile Crowd Sensing-enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance

1) Unlike MCS applied in an urban area, task allocation in a rural area cannot use commercial maps to plan paths directly. Inspired by the natural partition of farmland, this work establishes a novel mathematical farmland model by transforming a plane geographic map of farmland to a set of points.

2) We transfer the SILMP into an orienteering and combinatorial optimization problem to allocate appropriate tasks to participants toward efficient maintenance of the SILs. The purpose of optimization is to maximize the insecticide efficiency of SILs while minimizing total task cost. Then, using the natural partitioning of farms and the concept of face routing, we propose Partition-based Optimal Path Planning (POPP) to rapidly identify the optimum path and Cost-Efficiency-Balance-based Task Allocation (CEBT) to meet the designed goal.

3) The performance of the proposed methods is evaluated using synthetic data based on both the agriculture model and the mobility model.

[1] Yuanhao Sun, Edmond Nurellari, Weimin Ding, Lei Shu, Zhiqiang Huo, “A Partition-based Mobile Crowd Sensing-enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance”, IEEE Internet of Things Journal, 2022. DOI: 10.1109/JIOT.2022.3175732

SA1D-CNN: A Separable and Attention Based Lightweight Sensor Fault Diagnosis Method for Solar Insecticidal Lamp Internet of Things

1) A lightweight 1D-CNN-based sensor fault diagnosis scheme for SIL-IoTs nodes is proposed, namely, separable and attention 1D-CNN (SA1D-CNN). By diagnosing faults in SIL-IoTs nodes, fault states are transmitted to the cloud rather than all measurements. In this work, we focus on a lightweight fault diagnosis scheme rather than an exhaustive survey of fault types in SIL-IoTs nodes.
2) Because data fluctuation cases are caused by high voltage pulse discharge of the SIL (no-fault state and may lead to the misdiagnosis of faults), we propose the time-channel attention module (TCAM) to enhance fault-related features and suppress irrelevant features.
3) To reduce diagnostic delay and data transmission, a lightweight and easy implementing fault diagnosis model (namely SA1D-CNN) is embedded and performed in the SIL-IoTs node for the first time. SA1D-CNN is composed of depthwise separable convolution and TCAM.
4)The diagnostic accuracy of the proposed method is verified in SIL-IoTs nodes. The results indicate that the proposed method has the best trade-off performance between diagnostic accuracy and computationally efficiency than the referenced algorithms.

[1] Xing Yang, Lei Shu, Kailiang Li, Zhiqiang Huo, Yu Zhang. SA1D-CNN: A Separable and Attention Based Lightweight Sensor Fault Diagnosis Method for Solar Insecticidal Lamp Internet of Things[J]. IEEE Open Journal of the Industrial Electronics Society, 2022. doi: 10.1109/OJIES.2022.3172899.

Target-Barrier Coverage Improvement in an Insecticidal Lamps Internet of UAVs

We combine the advantages of UAVs and ILs-IoTs to construct a new IL-IoUAVs framework in the practical applications of pest control for smart agriculture.

We formulate the optimisation problem of the deployment as the minimisation of the number of IL-UAVs for constructing the target-barrier coverage, which the first study of the probabilistic sensing model in the target-barrier coverage problem, to the best of our knowledge.

We propose a deterministic deployment strategy to solve the optimisation problem for the deployment of IL-UAVs. After we make the analysis of the target-barrier circles and its properties, a merged algorithm is derived to construct the optimal $\epsilon$-target-barrier coverage.

Fig. 1: Our designed IL-UAV node and the architecture of an IL-IoUAV.

Pengju Si, Zhumu Fu, Lei Shu*, Yuli Yang, Kai Huang and Ye Liu. Target-Barrier Coverage Improvement in an Insecticidal Lamps Internet of UAVs[J]. IEEE Transactions on Vehicular Technology, 2022.

Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities

Compared with other surveys in the field, this survey provides a deeper summary of the universal and specific features of the typical equipment to enable researchers to have a comprehensive and quick understanding of equipment with a strong research foundation.
 
We provide an overview of anti-theft and anti-vandalism schemes. Moreover, we explore and discuss how to adopt AI solutions.
 
We present future research opportunities which contribute to locating, modeling, evaluating, and managing IoT equipment.
 

Xing Yang, Lei Shu*, Ye Liu, GP. Hancke, Ferrag Mohamedamine, Kai Huang. Physical Security and Safety of IoT Equipment: A Survey of Recent Advances and Opportunities​[J]. IEEE Transaction on Industrial Informatics, 2021.

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture - A Vision

(i) Based on the deployed MCS system, we review the typical MCS’s systems for different urban scenarios. Compared with the existing agricultural data collection methods, i. e., RSS, UAV, WSN, and CS, we find that AMCSs have significant benefits in flexibility, collecting implicit data, and low-cost requirements. However, we also note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work.

(ii) We analyze the crucial factors of combining MCS with agriculture. These factors, including the number of potential users, developed agriculture-related APPs, farmer’s experience, and cooperation between agribusiness and farmers, exist implicit relationships. Farmers, who collaborate with agribusiness, are willing to participate in the sensing task of AMCS-enabled agriculture, sharing their collected data to the AMCS platform with the APPs. Thus, agribusiness can also acquire extensive and valuable data from the AMCS platform or even farmers directly to improve their equipment and techniques. Meanwhile, agribusiness can offer more advanced technical guidance to help farmers increase income. Thus, positive feedback will form in this way.

(iii) We propose seven application scenarios of AMCS based on existing agricultural problems. By analyzing agricultural characteristics, we present several general research issues of AMCS for the future. Specially, we take the problem of maintaining SILs as a special case to discuss the specific research in a natural scene.

Yuanhao Sun, Weimin Ding, Lei Shu, Kailiang Li, Yu Zhang, Zhangbing Zhou, Guangjie Han, On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture – A Vision[J]. IEEE Systems Journal, 2021.

Design and Prospect of Anti-theft and Anti-destruction of Nodes in Solar Insecticidal Lamps Internet of Things

Based on the serious fact of theft and destruction of solar insecticidal lamp (SIL) in a long time, the anti-theft and anti-destruction hardware system of SIL is designed (Fig.1), and unmanned aerial vehicle insecticidal lamp (UAV-IL) (Fig.2), as an auxiliary equipment, is added to realize the anti-theft and anti-destruction of SIL in Solar Insecticidal Lamps Internet of Things (SIL-IoTs).

Kai Huang, Lei Shu*, Kailiang Li, Xing Yang, Yan Zhu, Xiaochan Wang, Qin Su. Design and Prospect of Anti-theft and Anti-destruction of Nodes in Solar Insecticidal Lamps Internet of Things[J]. Smart Agriculture, 2021. DOI: 10.12133/j.smartag.2021.3.2.202102-SA034. (In Chinese with English abstract)

(黄凯, 舒磊, 李凯亮, 杨星, 朱艳, 汪小旵, 苏勤. 太阳能杀虫灯物联网节点的防盗防破坏设计及展望[J]. 智慧农业(中英文), 2021, 3(1): 129-143.)

Optimal Deployment of Solar Insecticidal Lamps over Constrained Locations in Mixed-Crop Farmlands

Fan Yang, Lei Shu*, Yuli Yang, Guangjie Han, Simon Pearson, Kailiang Li. Optimal Deployment of Solar Insecticidal Lamps over Constrained Locations in Mixed-Crop Farmlands[J].  IEEE Internet of Things Journal, 2021, 8(16):13095-13114.

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

1) The development status of smart agriculture is summarized and classified into three typical development modes: precision agriculture, facility agriculture, and order agriculture. Furthermore, 7 key technologies and 11 key applications are
discussed.

2) Security and privacy countermeasures are summarized as (1) authentication and access control, (2) privacy-preserving, (3) blockchain-based solutions for data integrity, (4) cryptography and key management, (5) physical countermeasures, and (6) intrusion detection systems.

3) Potential security challenges of smart agriculture are highlighted and divided into two aspects: (1) agricultural production and (2) information technology.

4) Agricultural equipment will also affect the security strategy. For instance, it is suggested that high voltage discharge interference of Solar Insecticidal Lamps Internet of Things should be considered as attacks or have an impact on security strategy. We found that high voltage discharge interference has an impact on data acquisition from the results of our additional experiments in this paper. 

Xing Yang, Lei Shu*, Jianing Chen, Mohamed Amine Ferrag, Jun Wu, Edmond Nurellari, Kai Huang. A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges[J]. IEEE-CAA Journal of Automatica Sinica, 2021, 8(2): 273-302.

Improved Coverage and Connectivity via Weighted Node Deployment in Solar Insecticidal Lamp Internet of Things

Fan Yang, Lei Shu*, Yuli Yang, Ye Liu, Timothy Gordon. Improved Coverage and Connectivity via Weighted Node Deployment in Solar Insecticidal Lamp Internet of Things[J].   IEEE Internet of Things Journal, 8(12):10170-10186.

Collaborative Industrial Internet of Things for Noise Mapping:Prospects and Research Opportunities

Exposure to environmental noise has harmful effects on human health from both physiological and mental aspects, such as annoyance, sleep disorders, cardiovascular disease, or even permanent hearing impairment. This problem is further exacerbated in the industrial environments, where millions of workers are exposed to occupational noise. Therefore, noise mapping is essential and the first step to solve noise pollution problem.

However, either sound level meter-based measurement or computational model-based simulation, the two main noise mapping approaches at present, has its own limitations. To promote the development of noise mapping techniques, this article presents:

  • The framework of collaborative Industrial Internet of Things (IIoT) for next-generation noise mapping, especially in industrial parks.
  • Moreover, other potential applications beyond noise mapping for smart factory are listed.
  • Lastly, the fundamental issues and suggestions for future research are discussed in detail.

Ye Liu, Lei Shu*, Zhiqiang Huo, Kim-Fung Tsang, Gerhard P. Hancke. Collaborative Industrial Internet of Things for Noise Mapping:Prospects and Research Opportunities.  IEEE Industrial Electronics Magazine, 2021, 15(2):52-64.

High Voltage Discharge Exhibits Severe Effect on ZigBee-based Device in Solar Insecticidal Lamps Internet of Things

The main contributions are summarized as follows for many research communities, e.g., Internet of Things (IoTs), WSNs, reliability, and security:

1) If there is not a special design for a device that can release high voltage pulse discharge, we should not deploy the WSNs node nearby the device;

2) The use of the above device, as a method to attack WSNs, can lead to the abnormal working state.

[1] Kai Huang, Kailiang Li, Lei Shu*, Xing Yang. Demo Abstract: High Voltage Discharge Exhibits Severe Effect on ZigBee-based Device in Solar Insecticidal Lamps Internet of Things. IEEE International Conference on Computer Communications (IEEE INFOCOM 2020), July 7th-9th, 2020, Virtual Conference.

[2] Kai Huang, Kailiang Li, Lei Shu*, Xing Yang, Timothy Gordon, Xiaochan Wang*. High Voltage Discharge Exhibits Severe Effect on ZigBee-based Device in Solar Insecticidal Lamps Internet of Things. IEEE Wireless Communications, 2020, 27(6): 140-145.

Photovoltaic Agricultural Internet of Things towards Realizing the Next Generation of Smart Farming

Kai Huang, Lei Shu*, Kailiang Li, Fan Yang, Guangjie Han, Xiaochan Wang*, Simon Pearson. Photovoltaic Agricultural Internet of Things towards Realizing the Next Generation of Smart Farming[J], IEEE Access, 2020, 8: 76300-76312.

From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges

Ye Liu, Xiaoyuan Ma, Lei Shu, Gerhard Hancke, Adnan M. Abu-Mahfouz, From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges[J]. IEEE Transactions on Industrial Informatics, 2021, 17(6):4322-4334.

Internet of Things for Noise Mapping in Smart Cities: State-of-the-Art and Future Directions

YE LIU_IEEE Network_噪音地图

Ye Liu, Xiaoyuan Ma, Lei Shu, Qing Yang, Yu Zhang, Zhiqiang Huo, Zhangbing Zhou, Internet of Things for Noise Mapping in Smart Cities: State-of-the-Art and Future Directions[J]. IEEE Network, 2020, 34(4):112-118.

A Partition-Based Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things

Fan Yang, Lei Shu*, Kai Huang, Kailiang Li, Guangjie Han, Ye Liu. A Partition-Based Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things[J]. IEEE Internet of Things Journal, 2020, 7(11): 11223-11237.

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

Mohamed Amine Ferrag, Lei Shu*, Xing Yang, Abdelouahid Derhab, Leandros Maglaras. Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges[J].  IEEE Access, 2020, 8: 32031-32053.

Characteristics analysis and challenges for fault diagnosis in solar insecticidal lamps Internet of Things

Xing Yang, Lei Shu*, Kai Huang, Kailiang Li, Zhiqiang Huo, Yanfei Wang, Xinyi Wang, Qiaoling Lu, Yacheng Zhang. Characteristics Analysis and Challenges for Fault Diagnosis in Solar Insecticidal Lamps Internet of Things[J], Smart Agriculture, 2020, 2(02): 11-27. (In Chinese with English abstract)

(杨星, 舒磊, 黄凯, 李凯亮, 霍志强, 王彦飞, 王心怡, 卢巧玲, 张亚成. 太阳能杀虫灯物联网故障诊断特征分析及潜在挑战[J]. 智慧农业(中英文), 2020, 2(2): 11-27.)

Solar Insecticidal Lamps Internet of Things: Review and Open Research Issues

Kailiang Li, Lei Shu, Kai Huang, Yuanhao Sun, Fan Yang, Yu Zhang, Zhiqiang Huo, Yanfei Wang, Xinyi Wang, Qiaoling Lu, Yacheng Zhang. Research and Prospect of Solar Insecticidal Lamps Internet of Things[J]. Smart Agriculture, 2019, 1(3) : 13-28. (In Chinese with English abstract)

(李凯亮, 舒磊, 黄凯, 孙元昊, 杨帆, 张宇, 霍志强, 王彦飞, 王心怡, 卢巧玲, 张亚成. 太阳能杀虫灯物联网研究现状与展望[J]. 智慧农业(中英文), 2019, 1(3): 13-28.)