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  • Economics  (3)
  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2019
    In:  Mobile Information Systems Vol. 2019 ( 2019-09-17), p. 1-17
    In: Mobile Information Systems, Hindawi Limited, Vol. 2019 ( 2019-09-17), p. 1-17
    Abstract: Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
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  • 2
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-7-30), p. 1-8
    Abstract: As the digital economy promotes economic growth, the Internet of Things can solve the problem of productivity, and the blockchain can solve the problem of production relations, which realizes the industrial transformation of blockchain smart contract technology and becomes a new driving force for the industry. It is becoming more and more difficult for companies to control their own raw material supply, production, and sales by relying only on their own strength, and this is the key to influencing a company to become bigger and stronger. This problem will be efficiently solved by the implementation of “Internet of Things + Blockchain” technology. As a result, research into a new sort of smart supply chain management based on “Internet of Things + Blockchain” is required. From the perspective of building a smart supply chain, this paper makes full use of literature data methods, theoretical analysis methods, case analysis methods, logic analysis methods, and other methods. To study the effectiveness of IoT and blockchain technology through case study of changes in the order quantity in the procurement link of the supply chain. By understanding the current status of related research at home and abroad, as well as the Internet of Things technology and blockchain technology, this paper analyzes China. The main problems existing in the supply chain management of enterprises are the combination of the Internet of Things technology and the blockchain with the enterprise supply chain to create a smart supply chain platform, and the feasibility and functional efficiency of the smart supply chain platform. The related check was found, its deficiencies were found, and remedial measures were taken. The research results show that this intelligent supply chain management platform based on the Internet of Things (IoT) and blockchain can make the operation of the entire supply chain clearly visible. Information and data sharing can be achieved among the various departments of the supply chain to achieve scientific management and precision of the enterprise prediction. And this model can also be used in other industries to achieve industrial upgrading.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2018
    In:  Mobile Information Systems Vol. 2018 ( 2018), p. 1-9
    In: Mobile Information Systems, Hindawi Limited, Vol. 2018 ( 2018), p. 1-9
    Abstract: In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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