Chao-Lin Wu

Service-Oriented Smart-Home Architecture Based on OSGi and Mobile-Agent Technology

The architecture of a conventional smart home is usually server-centric and thus causes many problems. Mobile devices and dynamic services affect a dynamically changing environment, which can result in very difficult interaction. In addition, how to provide services efficiently and appropriately is always an important issue for a smart home. To solve the problems caused by traditional architectures, to deal with the dynamic environment, and to provide appropriate services, we propose a service-oriented architecture (SOA) for smart-home environments, based on Open Services Gateway Initiative (OSGi) and mobile-agent (MA) technology. This architecture is a peer-to-peer (P2P) model based on multiple OSGi platforms, in which service-oriented mechanisms are used for system components to interact with one another, and MA technology is applied to augment the interaction mechanisms

An integrated, flexible, and Internet-based control architecture for home automation system in the Internet era

In recent years, electronic appliances can be monitored and controlled by embedded microprocessors and be displayed on terminals, but they are still in lack of integration. Since the present home automation (HA) system is not equipped with efficient integration mechanism, it cannot fully manifest the worth of these developments. In order to achieve this goal of integration, many appliance manufacturers focus on the development of intelligent (or information) appliances to be integrated into a complete HA system for monitoring and controlling. Due to the advent of advanced computer and wideband network, the personal computer-based environment seems to be a very suitable platform for system integration. The personal computers can be linked by the network and are capable of powerful computation and easy display. We can take advantage of such abilities to develop an integration system.

Design and Realization of a Framework for Human–System Interaction in Smart Homes

The current smart home is a ubiquitous computing environment consisting of multiple autonomous spaces, and its advantage is that a service interacting with home users can be set with different configurations in space, hardware, software, and quality. As well as being smart technologically speaking, a smart home should also never forget to retain the “home nature” when it is serving its users. In this paper, we first analyze the relationship among services, spaces, and users, and then we propose a framework as well as a corresponding algorithm to model their interaction relationship. Later, we also realize the human-system interaction framework to implement a smart home system and develop “pervasive applications” to demonstrate how to utilize our framework to fulfill the human-centric interaction requirement of a smart home. Finally, our preliminary evaluations show that our proposed work can enhance the performance of the human-system interaction in a smart home environment.

WLAN location determination in e-home via support vector classification

Because of the advanced development in computer technology, home automation system could provide a variety of convenient and novel services to people. But only providing many kinds of services is not enough; instead, upgrading the quality of services is also a very important issue. One way to upgrade the service quality is to customize the service according to the inhabitants personal situation, and the user location is the key information for the home automation system to customize the services. Another impact of the advanced computer technology is to make the personal digital device to commonly have the capability to communicate through the wireless networks, and the popularity of wireless networks in home has increased in recent years. As a result, home automation system can bring services to personal digital devices held by people through any wireless network, and customize the services according to the location of personal digital device in home. In this paper, we present a location determination system for the home automation system to provide location aware services. This location determination system uses support vector machine to classify the location of a wireless client from its signal strength measures, and we describe its architecture and discuss its performance.

A Reciprocal and Extensible Architecture for Multiple-Target Tracking in a Smart Home

Every home has its own unique considerations for location-aware applications. This makes a flexible architecture very crucial for efficiently integrating various tracking devices/models for adapting to real human needs. Here, we propose a reciprocal and extensible architecture to flexibly add/remove tracking sensors/models for tracking multiple targets in a smart home. Regarding tracking devices, we employ sensors from two different categories, those with seamless sensors and those with seamful ones. This allows us to take human-centric needs into consideration and to facilitate reciprocal and cooperative interaction among sensors from the two categories. Such reciprocal cooperation aims to increase the accuracy of location estimates and to compensate for the limitations of each sensor or a tracking algorithm, which allows us to track multiple targets simultaneously in a more reliable way. Moreover, the approach demonstrated in this paper can serve as a guideline to help users customize sensor arrangements to fulfill their requirements. Our experimental results, which comprise three tracking scenarios using a load sensory floor as the seamless sensor and RF identifications (RFIDs) as seamful sensors, demonstrate the effectiveness of the proposed architecture.

Context-aware home energy saving based on Energy-Prone Context

Energy overuse has caused many environmental and economic issues, so energy saving for household is challenging and important for a smart home. For home energy saving based on context-awareness, human activity is critical information since knowing what activities are undertaken is important for judging if energy consumed by appliances is well spent by users. Such contextual information is an important clue for providing an energy saving service. However, most of the prior works on home energy saving often ignore those appliances which are operating indirectly or implicitly related to the context. These factors may compromise the practicality and acceptability of most of the currently available energy saving systems, thus failing to meet real user needs. Therefore, we propose utilizing an Energy-Prone Context to model a context and its associated energy consumption. In addition, we also propose a systematic method to determine energy-saving services based on the Energy-Prone Contexts. Our experimental results demonstrate the effectiveness of the proposed approach.

Energy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Environment

Human activity is among the critical information for a context-aware energy saving system since knowing what activities are undertaken is important for judging if energy is well spent. Most of the prior works on energy saving do not make the best of context-awareness especially in a multiuser environment to assist the energy saving system. In addition, they often ignore whether appliances are operating implicitly or explicitly related to the context. These factors may compromise the practicality and acceptability of most of the currently available energy saving systems, thus failing to meet real user needs. Therefore, we propose Energy-Responsive Aggregate Context (ERAC) to model multi-resident activities and their associated energy consumption. Based on the relationship, implicit or explicit, between a given appliance and its associated context, an energy saving system and its users can better determine whether the power consumed by the appliance is wasted. Our experimental results demonstrate the effectiveness of the proposed approach.

A human-system interaction framework and algorithm for UbiComp-based smart home

Current smart home is a ubiquitous computing environment consisting of multiple agent-based autonomous spaces, and it brings both advantage and challenge that a service interacting with users can be performed with multiple choices of configuration in space, hardware, software, and quality. And besides being smart in the technology phase, a smart home should also satisfy the ldquohomerdquo feeling when interacting with its inhabitants. In this work, from the perspective of human-computer interaction, we analyze the relationship between services, spaces, and users, and then propose a framework as well as a corresponding algorithm to model their interaction, thus for a smart home to behave both smart and as a home when interacting with its inhabitants.

Spatio-temporal Feature Enhanced Semi-supervised Adaptation for Activity Recognition in IoT-Based Context-Aware Smart Homes

One of the most important applications for Internet of Things (IoT) is smart homes, where user activities generate lots of data from the interaction with IoT-based home environments. These rich interaction data can be further processed and modeled as useful contexts thus for smart homes to provide appropriate services accordingly, which in turn makes activity recognition (AR) an essential part. Traditionally, smart homes will train an AR model for all the user behaviors in advance, and then use semi-supervised learning to update AR models to deal with the changes of user behaviors while minimizing user efforts. However, if a user behavior changes too much for its original AR model, semi-supervised learning may fail to select representatives from its resulting activity instances for model adaptation, and thus causing poor performance of smart homes. In this work, we propose an approach to make use of spatial features together with temporal features to further discover more useful representative activity instances for semi-supervised learning to do AR model adaptation. Our experimental results demonstrate the effectiveness of the proposed approach.

Mobile agent based integrated control architecture for home automation system

The architecture of the conventional home automation system is usually centralized and thus causes many problems, e.g. lots of network traffic, heavy computation load, and poor fault tolerance. Another problem with the architecture is that there exists a variety of different home control networks incompatible with one another, which makes it difficult to integrate them in order to provide high level management functions in a home automation system. Besides the problems mentioned above, the most serious problem with a home automation system is its dynamically changing environment. Such frequently changing situation of the peripherals disallows straightforward configuration and maintenance of the home automation system. In this paper, we propose a mobile agent based integrated control architecture for home automation system, which is able to solve the problem mentioned above. This architecture is a distributed one, reducing the network traffic and computation load by delegating the management function to each control node. To integrate various kinds of home control network, we use computer network as the backbone and apply the concept of gateway to facilitate different networks to communicate with one another. Finally, we take advantages of the characteristics of mobile agent to cope with the problem of the dynamic environment and to enhance the fault tolerance mechanism.