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  • Hindawi Limited  (2)
  • Chen, Ying  (2)
  • Zhao, Jie  (2)
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  • Hindawi Limited  (2)
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  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-8-5), p. 1-10
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-8-5), p. 1-10
    Abstract: Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-10-8), p. 1-15
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-10-8), p. 1-15
    Abstract: Urban Internet of Things (IoT) plays an extremely important role in our daily life by deploying smart cities and urban brains. Orthogonal multiple access (OMA) technology has been a commonly used communication method in recent years, but nonorthogonal multiple access (NOMA) attracts the attention of many researchers due to its superiority of successive interference cancellation (SIC) technology. We consider adding the base station (BS) and unmanned aerial vehicle (UAV) to perform collaborative data offloading services with urban IoT devices and introduce the NOMA technology to improve offloading efficiency. In order to solve the data unloading problem in this model cost-effectively, we formulate the model as a game model based on noncooperative competition and propose the iterative game-based data offloading algorithm (GDOA) to obtain the Nash equilibrium (NE) solution. Finally, we use the simulation data to conduct parametric analysis experiments and comparison experiments on GDOA to evaluate its real performance.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
    Location Call Number Limitation Availability
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