Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

Guillén, Miguel Ángel; Llanes, Antonio; Imbernón, Baldomero; Martínez‐España, Raquel; Bueno-Crespo, Andrés; Cano, Juan-Carlos; Cecilia, José M.

doi:10.1007/s11227-020-03288-w

Cited by 66 publications

(32 citation statements)

References 30 publications

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“…There have been some suggested methodologies for bringing fog processing for AI in smart agriculture, for, e.g., in [103] a deep learning entrusted to fog nodes (DLEFN) algorithm is described to support efficient use of resources and reduce cloud resource usage. However, as noted in [104], who use an edge system for temperature prediction using an LSTM, edge device performance still lacks that of similar cloud systems but the inclusion of DL capable hardware does provide opportunities for further innovations. Previous work by the same author [105], where they aimed to monitor crops for frost signs and trigger anti-frost measures, compared edge and cloud computing systems for outlier detection and determined that cloud implementations to provide much better performance.…”

Section: Smart Agriculturementioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Agriculturementioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…These services usually require many mobile devices with limited size and low power consumption to perform computationally intensive and timesensitive tasks [26]. However, due to the low computing power and limited battery life of mobile devices, it is challenging to perform these services [27,28]. Edge computing is an advanced version of cloud computing that reduces latency by bringing the service closer to the end user.…”

Section: Edge Computing Technologymentioning

confidence: 99%

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

Sun

Gao

2021

Wireless Communications and Mobile Computing

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Educational informatization has become the only way for mankind to enter the information age, and the application of educational resource library is a key issue in the integration of information technology disciplines. Aiming at the shortcomings of existing college physical education management technology, this article is dedicated to developing a new online management platform for physical education. Firstly, the introduction of edge computing technology in the architecture design of college physical education system can fully improve the efficiency of the management system. Secondly, based on data cleaning, the BP neural network algorithm modified by particle swarm is used to conduct in-depth analysis of the data of the university sports teaching management system and obtain corresponding optimization measures. Finally, it is verified in actual tests that the system can quickly perform data search, entry, modification, deletion, and other operations, which can ensure the realization of the school sports department’s networked, scientific, standardized, and digitalized management of physical education resources.

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“…A schematic diagram of the filtered projection polar coordinate geometry is shown in Figure 3. It can be seen from the above formula that the projection length is no longer dependent on this angle, so the area to be solved for the image is transformed from the plane with XY as the horizontal and vertical coordinates to the polar coordinate plane with the length as the rotation angle [37]. In the process of CT image reconstruction, it is first converted from the XY coordinate domain to the polar coordinate domain and then reconstructed [38].…”

Section: Optimization Of Ct System Data Formentioning

confidence: 99%

Research on Data Optimization of CT System for Patients with Severe Pneumonia Based on Filtered Back Projection and Internet of Things

Qin

Wang

2021

Wireless Communications and Mobile Computing

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CT technology can clearly and intuitively display the internal structure, composition, material, and defect status of the detected object under the condition of no damage to the detected object. This paper is based on the filtering back projection and the Internet of Things technology to carry out the data optimization of the CT system of patients with severe pneumonia. First of all, the Internet of Things technology is used in the structural design and operation process of the CT system for patients with severe pneumonia, which improves the advancement and convenience of the original system. Secondly, the system data is optimized through the filtering back projection algorithm, which improves the data accuracy and transmission efficiency. Finally, simulation tests have verified the effectiveness of the established CT system data optimization method for patients with severe pneumonia, which is helpful for medical comparison, analysis, and diagnosis.

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Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

Cited by 66 publications

References 30 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

The Design of University Physical Education Management Framework Based on Edge Computing and Data Analysis

Research on Data Optimization of CT System for Patients with Severe Pneumonia Based on Filtered Back Projection and Internet of Things

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