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Sensor nodes in a wireless sensor network establish a network based on location information, set a communication path to the sink for data collection, and have the characteristic of limited hardware resources such as battery, data processing capacity, and memory. The method of estimating location information using GPS is convenient, but it is relatively inefficient because additional costs accrue depending on the size of space. In the past, several approaches including range-based and range-free have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. In addition, this study lowered the error rate resulting from difference in response time by adding reliability for calculating and compensating detailed location information using inertia.

As fashion E-business is coming, it is becoming important to provide the analysis of preferences that is becoming increasingly more customer oriented. Consumers caused the diversification of the fashion product because they seek fashion and individuality in order to satisfy their needs. In this paper, we proposed the quick response system using the collaborative filtering on fashion E-business. The proposed method applies the developed quick response system to increase the efficiency of merchandising for the products of design styles. Collaborative filtering was adopted in order to recommend final design styles of interest for designers based on the predictive relationship discovered between the current designer and other previous designers. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity of our system.

Users in ubiquitous environments can use dynamic services whenever and wherever they are located because these environments connect objects and users through wire and wireless networks. Also, there are many devices and services in these environments. However, it is difficult to effectively use conventional filtering method of the recommendation system in future ubiquitous environments because it does not reflect context information well in these environments. This paper attempt to define context model and propose new Collaborative Filtering (CF) based on Hidden Markov Models (HMMs) that are trained by context information. The Collaborative Filtering using HMMs (CFH) is suited to a user's interests and preferences. The Ubiquitous Recommendation System (URS) used in this study based on CFH uses an Open Service Gateway Initiative (OSGi) framework to recognize context information and connect device in smart home.

Music recommendation systems used at the present time apply certain queries using appropriate music information or user profiles in order to obtain the desired results. However, these systems are unable to satisfy user desires because these systems only reply to the results of user queries or consider static information, such as a user’s sex and age. In order to solve these problems, this paper attempts to define context information to select music and design a music recommendation system that is suited to a user’s interests and preferences using a filtering method. The recommendation system used in this study uses an Open Service Gateway Initiative (OSGi) framework to recognize context information. Not only does this framework promote a higher user satisfaction rate for music recommendations, service quality is also improved by applying service mobility and distributed processing.