Edge computing and the internet of things on agricultural green productivity

Shi, Hongyu; Li, Qiubo

doi:10.1007/s11227-022-04463-x

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In July 2018, the CPC Central Committee and The State Council issued the Strategic Plan for Rural Vitalization (2018-2022), marking the full implementation of rural vitalization [3] . On September 5, 2019, the General Secretary wrote back to the secretaries, presidents of agriculture-related universities, and experts, raising ardent expectations for the development of higher agriculture and forestry education in the new era [4] . Yan Wu, director of the Higher Education Department of the Ministry of Education, said that the construction of the new agricultural science technology step by step, singing the "trilogy."…”

Section: Research Backgroundmentioning

confidence: 99%

Research on the Strategic Path of Veterinary Medicine Serving Rural Revitalization in Agriculture-Related Universities

Liao,

Yu,

Tang

et al. 2024

JCER

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Local agriculture-related universities must have certain regional advantages, advantages of agricultural characteristics, human resources, and science and technology in the process of implementing the rural revitalization strategy. Using literature research and investigation methods, combined with the experience of colleges and universities, the veterinary discipline of rural science and technology correspondent and technology yard, this study discusses our veterinary discipline in education teaching practice and talent training mode innovation, improving the quality of talent training and study on the strategic path of united front service for rural revitalization.

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Section: Research Backgroundmentioning

confidence: 99%

Research on the Strategic Path of Veterinary Medicine Serving Rural Revitalization in Agriculture-Related Universities

Liao,

Yu,

Tang

et al. 2024

JCER

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“…Sagarika Paul et al used machine learning techniques such as linear regression, support vector machine regression, PCA, and naïve Bayes to predict the soil moisture 12 to 13 weeks in advance [ 24 ]. Hongyu Shi et al used deep reinforcement learning (DRL) to optimize task scheduling at the edge of the IoT and proposed an EC-AIoT CMS based on DRL optimization [ 25 ]. Sonia Naderi et al proposed a low-cost, reliable wireless soil moisture sensing system to enable efficient spatial–temporal data collection, in which a random forest, a Gaussian process, and a support vector regressor were used to calibrate the system [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

2023

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The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

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“…These data frequently contain personal data like IP addresses, unique device or application identifiers, and location data. Federated learning (FL) paradigm, which provides a practical strategy for developing distributed intelligent systems that protect privacy, addresses this issue [3]. Additionally, recent research has demonstrated that analysis methods trained in federated mode perform similarly when it comes to anomaly and attack detection.…”

mentioning

confidence: 99%

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

Boopathi

Gupta

et al. 2023

Preprint

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Edge computing (EC) has been viewed as a viable option for addressing cloud computing's shortcomings in supporting Internet-of-Things (IoT) applications as an innovative strategy. However, edge-assisted IoT security requires to be better guaranteed due to the network's instability and increase in attack surfaces. Practice of safeguarding a given network from threats that could compromise its availability is referred to as network security. In addition, we can add that network security must address issues such as unauthorized access to network-accessible resources and even their misuse. This study proposes a novel approach to improving edge computing-based data privacy through secure data transmission and deep learning-based optimization with Internet of Things. Edge network privacy preservation is accomplished through collaborative architecture and hybrid federated sever-based stochastic vector networks. The trust based multiple encryption algorithm is used to secure the transmission of the data after it has been optimized using firefly grey optimization. Throughput, network security analysis, PDR, latency, and energy consumption were all examined as part of the experimental analysis for network data privacy and optimization.

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Edge computing and the internet of things on agricultural green productivity

Cited by 7 publications

References 22 publications

Research on the Strategic Path of Veterinary Medicine Serving Rural Revitalization in Agriculture-Related Universities

Research on the Strategic Path of Veterinary Medicine Serving Rural Revitalization in Agriculture-Related Universities

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

Optimization algorithms in security and privacy-preserving data disturbance for collaborative edge computing social IoT deep learning architectures

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