Artificial intelligent system for multimedia services in smart home environments
| Autor: | Jose M. Jimenez, Albert Rego, Pedro Luis Gonzalez Ramirez, Jaime Lloret |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Service (systems architecture)
Computer Networks and Communications Computer science 020209 energy 02 engineering and technology computer.software_genre Field (computer science) User experience design Smart home Home automation Reinforcement learning 0202 electrical engineering electronic engineering information engineering Computer communication networks Multimedia business.industry Deep learning 020206 networking & telecommunications INGENIERIA TELEMATICA Classification Artificial intelligence business Internet of Things computer Software |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| DOI: | 10.1007/s10586-021-03350-z |
| Popis: | [EN] Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10. This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially founded by the Universitat Polite`cnica de Vale`ncia through the postdoctoral PAID-10-20 program. |
| Databáze: | OpenAIRE |
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