In this paper, we propose a new user authentication (UA) scheme based on one-time password (OTP) protocol using smart cards for home networks. The proposed scheme is to authenticate home users who uses home devices. Several techniques using technology based on biometrics, passwords, certificates, and smart cards can be used for user authentication in the similar environments. However, such user authentication techniques must be examined before being employed in an environment where home devices have low efficiency and performance. Here, we present the important security functions of home networks. The proposed authentication protocol is designed to accept the existing home networks based on the one-time password protocol. Also, it is a well suited solution and is quite satisfactory in terms of the security requirements of home networks, because of requiring low computation by performing simple operations using one-way hash functions. Our proposed scheme can protect against illegal access for home services and devices and does not allow unnecessary service access by legitimate users. Therefore, it allows the user to provide real-time privilege control and good implementation in secure home networks.
Abstract. The home network is a new IT technology environment for making an offer of convenient, safe, pleasant, and blessed lives to people, making it possible to be provided with various home network services by constructing home network infrastructure regardless of devices, time, and places. This can be done by connecting home devices based on wire and wireless communication networks, such as mobile communication, Internet, and sensor network. However, there are many risks involved, for example user privacy violations and service interference. Therefore, security service is required to block these risk elements, and user authentication is an essential component for secure home network service.
This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development.
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