Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning

Zhang, Cong; Pang, Haitian; Liu, Jiangchuan; Tang, Shizhi; Zhang, Rui-Xiao; Wang, Dan; Sun, Lifeng

doi:10.1109/access.2019.2947067

Cited by 46 publications

(30 citation statements)

References 42 publications

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“…Videos combine images, sounds and words that can be presented in different ways and at different lengths to facilitate learning [32]. Many studies in recent years have shown the benefits of using video in the classroom [33,34], as the multimedia factor turns any new element in the learning process into long-term memory learning [35].…”

Section: Introductionmentioning

confidence: 99%

Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training

et al. 2020

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Information and communication technologies (ICTs) have led to the emergence of a variety of active and innovative teaching methods. This is the case in role-playing, which consists of simulating a real-life situation, in this case the school context, in which the student takes on a certain role and interacts with other students in a fictitious situation. Framed in this way, the present study aims to show if the application of the role-playing method promotes the improvement of attitude variables and practical skills. To this end, we advocated the use of a quasi-experimental methodology, with a control and experimental group and the application of a post-test. The sample is composed of 138 students from the Master of Teachers of Compulsory Secondary Education in Ceuta (Spain). The results showed that the students positively valued the application of the method, obtaining better scores in the set of variables studied, especially in motivation, creativity and collaboration. Therefore, it continues to be observed that the application of innovative methodologies through technology promotes the increase of multiple skills in the student body. This study aimed to prove that the use of active methods provides an increase in students’ skills, and that, therefore, we must bet on the use of sustainable pedagogies in order to promote a real innovation in the classrooms.

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Section: Introductionmentioning

confidence: 99%

Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training

et al. 2020

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“…Li et al [59] considered the video propagation as well as popularity evolution patterns and developed an integration of ARIMA, multiple linear regression (MLR), and k-nearest neighbor regression (kNN) to predict the social patterns to improve caching performance. Zhang et al [60] proposed an intelligent edge-assisted caching framework LSTM-C based on LSTM to better learn to content popularity patterns both at long and short time scale. Zhu et al [61] proposed to leverage DRL to automatically learn an end-to-end caching policy, where the user requests, network constraints, and external information are all embedded in the learning environment.…”

Section: ) Cachingmentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field. INDEX TERMS Internet of Things, edge computing, deep learning, intelligent edge applications.

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“…Zhou et al ( 2019 ) presented a system that predicts sunlight and estimates the electricity produced by solar power systems through LSTM, which becomes the basis of solar power equipment management. Finally, Zhang et al ( 2019 ) utilized LSTM in edge computing; when video files are too large, an LSTM technique helps predict the buffer memory for each edge computing, node. Guardo et al ( 2018 ) mainly used Fog Computing to reduce the workload of centralized servers.…”

Section: Related Workmentioning

confidence: 99%

Using Long Short-Term Memory for Building Outdoor Agricultural Machinery

Zhan

et al. 2020

Front. Neurorobot.

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Today, climate change has caused a decrease in agricultural output or overall yields that are not as expected; however, with the ongoing population explosion, many undeveloped countries have transformed into emerging countries and have transformed farmland to be used in other types of applications. The resulting decline in agricultural output further increases the severity of the food crisis. In this context, this study proposes an outdoor agricultural robot that uses Long Short-Term Memory (LSTM). The key features of this innovation include: (1) the robot is portable, and it uses green power to reduce installation cost, (2) the system combines the current environment with weather forecasts through LSTM to predict the correct timing for watering, (3) detecting the environment and utilizing information from weather forecasts can help the system to ensure that growing conditions are suitable for the crops, and (4) the robot is mainly for outdoor applications because such farms lack sufficient electricity and water resources, which makes the robot critical for environmental control and resource allocation. The experimental results indicate that the robot developed in this study can detect the environment effectively to control electricity and water resources. Additionally, because the system is planned to increase agricultural output significantly, the study predicts the variables through multivariate LSTM, which controls the power supply from the solar power system.

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Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning

Cited by 46 publications

References 42 publications

Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training

Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Using Long Short-Term Memory for Building Outdoor Agricultural Machinery

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