An adaptive offloading framework for Android applications in mobile edge computing

Chen, Xing; Chen, Shihong; Ma, Yun; Liu, Bichun; Zhang, Ying; Huang, Gang

doi:10.1007/s11432-018-9749-8

Cited by 44 publications

(22 citation statements)

References 31 publications

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“…Having rich features, but still, MobiCOP-IoT needs manual configuring to decide whether the structure should work in cloud or edge mode. A Mobile Edge Computing (MEC) [49], based on an adaptive framework that supports mobile applications with offloading is proposed. It enables the application to dynamically offload among the mobile devices, edges and the cloud.…”

Section: Offloading Frameworkmentioning

confidence: 99%

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

104

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The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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Section: Offloading Frameworkmentioning

confidence: 99%

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

104

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“…After taking a step, the distance between the centers of the feet is the stride. However, the stride length in running will vary according to different running styles, which is generally related to height [19] [20]. If it is a short-medium run, the stride length is about 80% of your height.…”

Section: Cadence and Stride Lengthmentioning

confidence: 99%

Research on intelligent analysis of running sports training performance based on artificial intelligence

Yang

Liu

Xu-zhong³

2021

Preprint

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With the increasing awareness of human beings in the pursuit of human health, running in sports has become a fashionable and healthy first choice. The research uses artificial intelligence technology to conduct intelligent analysis when running training, and aims at the use of artificial intelligence technology. Artificial intelligence technology can accurately analyze and predict the application requirements of sports training postures. We proposed a set of sports posture analysis and predict system to design in this paper. It uses the running training record data in the watch heart rate and GPS smart sports watch, and uses Recurrent Neural Network (RNN), Long and short-term memory (LSTM) and Gate recursive unit (GRU) that three types of neural network models to predict whether the road race can be in the conference. And confirm it will be completed within the scheduled closing time, and it will also perform intelligent analysis of physical fitness (heart rate, pace) and running technology (cadence, pace). The training and test data for this study are the running training records from (Running distance, time, heart rate, stride frequency, stride length, pace, calories, altitude and other characteristic values) as input parameters to test and compare the running completion time trend of RNN, LSTM, GRU neural network models in the exercise table predictive power. The results show predict accuracy is the best is GRU method, and the worst is LSTM method. After the hidden layers are added to the three predict models, RNN is slightly regressive, LSTM has a significant trend of improvement, and GRU is less obvious.

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“…Innovation and entrepreneurship education is an important measure used to cultivate the innovative spirit of college students and improve the quality of entrepreneurship and employment. For application-oriented colleges and universities, innovation and entrepreneurship education is both necessary and urgent in terms of the types and quality of the talent cultivation, the promotion of entrepreneurship, and the employment of college students (Chen et al, 2019a;Yang, 2020). As the core course of innovation and entrepreneurship education, Fundamentals of Entrepreneurship is a compulsory course based on advanced innovation and entrepreneurship education concepts and solid innovation and entrepreneurship education practice, meeting the skills needs of contemporary college students and future economic development (Huang et al, 2017;Liang et al, 2021;Zhao and Zhang, 2021).…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors of Student Satisfaction With the Teaching Quality of Fundamentals of Entrepreneurship Course Under the Background of Innovation and Entrepreneurship

2021

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Innovation and entrepreneurship education is a key way to cultivate applied talents in colleges and universities. This article aimed to optimize the teaching quality of the Fundamentals of Entrepreneurship courses by constructing a conceptual model of “teaching quality–student satisfaction.”Teaching quality is divided into four indicator elements, teaching content, teaching methods, teaching conditions, and teaching management. A student satisfaction questionnaire was designed to measure the teaching quality of a Fundamentals of Entrepreneurship course. The reliability and validity of survey data from Linyi University were analyzed using SPSS.20 software. Through correlation and multiple regression analysis, it can be seen that the teaching content, teaching methods, teaching conditions and teaching management have a significant positive correlation with the teaching quality of the Fundamentals of Entrepreneurship course, which is an important factor affecting student’s satisfaction, and there is a certain gap between the expectation of teaching quality and student’s satisfaction. On this basis, suggestions are put forward to improve student satisfaction with the teaching quality of Fundamentals of Entrepreneurship courses, and to provide empirical evidence and recommendations for continuously improving the teaching quality of Fundamentals of Entrepreneurship courses, thereby improving college students’ employment and entrepreneurship ability.

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An adaptive offloading framework for Android applications in mobile edge computing

Cited by 44 publications

References 31 publications

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Research on intelligent analysis of running sports training performance based on artificial intelligence

Influencing Factors of Student Satisfaction With the Teaching Quality of Fundamentals of Entrepreneurship Course Under the Background of Innovation and Entrepreneurship

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