Earticle

초록

영어

In this study, we analyze the service discovery protocol problem in a smart home environment and propose a deep learning-based prediction model. Currently, the service discovery protocols used in smart homes are divided into IP-based and non-IP-based, and there are limitations in service discovery owing to the interoperability issues between them. Although smart gateways support protocol conversion between heterogeneous networks, there is a limitation that smooth service discovery between smart home devices is difficult due to the lack of advertising and broadcasting functions for service discovery. To solve this problem, we propose a deep learning-based prediction model utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The experimental results show that the proposed model can accurately predict future service demands by learning various service request patterns and enables faster and more efficient service discovery than existing protocols. The proposed model has been shown to improve the interoperability between devices in a smart home environment that changes in real-time and significantly reduces the service discovery time.

목차

Abstract
1. Introduction
2. Related Work
2.1 Smart Home Control System
2.2 Existing Service Discovery Protocols
2.3 Deep Learning-Based Prediction Model
3. The Proposed Scheme
3.1 Deep Learning-based Smart Home Service Exploration System
3.2 Deep Learning-based prediction model
4. Performance Evaluation
5. Conclusion
References

키워드

저자

• Ducsun Lim [ Post Dr., Dept. of Computer Software, Hanyang University South University, Seoul, Korea ]
• Youn-A Min [ Professor, Dept. of Applied Software, Hanyang Cyber University South University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380