Taehun Kim

MiRE4OWL: Mobile Rule Engine for OWL

Mobile user devices, such as smart phones have become the most popular high-end mobile devices far beyond just cellular communication devices. They will continue evolving toward being palm-top computers with the rapid development of wireless communications and platform technologies. This paper introduces our development of a semantic reasoner called MiRE4OWL to be installed onto the resource-limited mobile user devices to accommodate context-aware ubiquitous computing services. It is a data-log and rule-based inference engine which performs semantic reasoning operations over the application semantics represented in OWL-DL. The mobile rule engine that underlies MiRE4OWL achieves light-weight design to meet the resource constraints of the mobile devices and yet achieves better expressiveness than existing engines. The performance evaluation showed that the intended functionality and resource efficiency have been fulfilled.

A Distributed Middleware for a Smart Home with Autonomous Appliances

This paper presents a distributed smart home middleware where each appliance is able to learn user behavior and customize their actions by themselves as well as cooperate with other appliances through a more light-weight smart home gateway. As the key components, we present a knowledge base which describes common- and appliance-specific concepts in a smart home domain, and design libraries for smart appliances and a smart home gateway. We implement the proposed middleware on our testbed and conduct evaluations. The result shows that our scheme reduces the interaction time and the runtime memory allocation.

A data acquisition architecture for healthcare services in mobile sensor networks

Mobile healthcare services have received growing attention due to the rapid technical advancement of sensor devices, sensor networks, and mobile data transmission. Healthcare monitoring services, in general, lack the needed sophistication for the acquisition of the huge amount of healthcare data generated from mobile users. The overhead of filtering out a large amount of unnecessary data that are not directly useful for the healthcare applications is unavoidable. In this paper, we propose a healthcare service architecture and introduce data acquisition software using a database querying facility as a solution to the problem. Our proposed service architecture with the data acquisition scheme effectively bridges the gap between the healthcare application domain and the acquisition of physical data.

SpinRadar: a spontaneous service provision middleware for place-aware social interactions

With the advancements of mobile phones and the integration of multiple communication interfaces, online social interaction between users is no longer restricted to a specific place with connectivity to the Internet but can happen anywhere and at any time. This has promoted the development of mobile social applications to enable opportunistic interactions with co-located users. One of the challenging problems in such interactions is to discover interaction opportunities with nearby users. Existing works focus on properties related to mobile users in order to find similar users in the surrounding area; these works depend on predefined logic such as conditional statements to recommend spontaneous social interaction opportunities. However, the social implications of the place in which the interaction is taking place are an important factor for recommendations, as those implications provide hints about the most plausible types of interactions among co-located users. In this work, we present a middleware called SpinRadar which is designed to support spontaneous interactions between co-located users by taking into account the semantics of a place, which we call ‘placeness.’ Our evaluation shows that the proposed scheme satisfies users much more than existing schemes.

Location-aware event-driven query processing in sensor database management

In the sensor database management, a query is disseminated from the base-station to the sensor nodes in a sensor network to collect various types of ambient data requested by applications. Sensor database systems typically support event-driven queries to be executed when some events are detected at some sensor nodes. In this paper, we propose a location-aware, event-driven query processing scheme using an in-network query processing algorithm as an extension to our previous work on Sensor Network Query Language (SNQL). The proposed scheme provides a couple of location-aware expressions for selective dissemination and in-network propagation of a query. We also propose a spatial metadata management scheme by using Quadtree. Our evaluation shows that energy consumption of our event-driven query processing is reduced by 45% and response time is faster by up to 50% compared to the existing systems.

Sherlock-SD: A light-weight universal service discovery for Web of Things (WoT) services

Since the Web of Things (WoT) term was first proposed, there has a big trend in IT vendors providing users with various services through their smart products. To enable users to discover and leverage these services, SDPs play an important role. However, so many variations of SDPs have been introduced it has caused a heterogeneity issue. Including standardization, many solutions have been proposed, but they require too much overhead or have practicality issues. In this paper, we propose a system composed of fundamental building blocks, including a knowledge base and probing packets to address this issue. We evaluate the performance of our system by conducting real world experiments using smart object services in a local network. The experiment results show that Sherlock-SD identifies up to 92% of target services correctly only with 3 probing packets out of 6 in the best case, without any of the overhead that existing solutions impose. In terms of resource consumption overhead, compared to 4 SDPs enumeration, Sherlock-SD requires 67.6% of the memory and consumes 30.7% of power.

A Multi-Dimensional Smart Community Discovery Scheme for IoT-Enriched Smart Homes

The proliferation of the Internet into every household has provided more opportunities for residents to become closer to each other than before. However, solid structural barrier is raised and social relationships within such neighborhoods are weak compared to those in traditional towns. Accordingly, activating communities and ultimately enhancing a sense of community through constructive participation and communal sharing of labor among residents has currently emerged as a challenging issue in a contemporary housing complex. In an effort to activate those communities, a notion of smart community is presented in which multiple smart homes are equipped with Internet of Things and interconnected with each other. Beyond the unadorned smart community composed by physical proximity, it is essential to discover a human-centric community that achieves communal benefits and enables residents to maximize individual economic gain by leveraging collective intelligence. In this article, we present a multi-dimensional smart community discovery scheme that enables householders to find human-centric community considering multi-dimensional factors in terms of physical, social, and economical aspects. We conduct experiments with 30 real households by applying a community-based energy saving scenario. Experiment results show that the proposed scheme performs better when compared to the physical proximity-based one in energy consumption and user satisfaction.

Place-aware opportunistic service recommendation scheme in a smart space with Internet of Things

In this paper, we present an opportunistic service recommendation scheme in a place with smart objects to provide users with appropriate composite services to accomplish their task goals. The proposed scheme infers possible tasks from interactions that people have experienced with smart objects including other places that are similar to a given place. The proposed scheme then evaluates the candidate tasks by measuring quality satisfaction of each task against given QoS constraints. We test the proposed scheme on our three smart testbeds where various smart objects equipped. Experiment results show that the proposed scheme finds potential tasks in the testbeds in a reasonable time.

A sensor network query processing system for healthcare data acquisition in Dr. M

In mobile healthcare monitoring services with wearable sensor devices and mobile communications networks, data acquisition is an important issue for the design and implementation of various healthcare application services. In this paper, we present an efficient healthcare data acquisition apparatus by accommodating the notion of query language and processing functions of the sensor network database system. We present our healthcare monitoring service architecture, query language, and data acquisition process. We show how our proposed system implements the intended healthcare data acquisition facility. Our prototype system has been evaluated by using some example queries and a healthcare dataset of an intensive care unit patients to verify the expressiveness of our query language and efficiency of the healthcare data acquisition to support the healthcare applications.

Friend Recommendation Using Offline and Online Social Information for Face-To-Face Interactions

The combination of online social networking services (OSNSs) and the smartphones has changed the ways to acquire information and also influenced the ways people get to know and interact with each other. Despite with many advanced functions, online social interactions can hardly provide online social users with real-world (i.e., offline) social interactions. In this paper, we present an offline friend recommendation system for the OSNS users to have more serendipitous offline interactions. For that, we modeled both offline information (i.e., place visit history) and online social data (i.e., friend relationships) for recommending their offline interactions. Our system discovers those who seems most likely to have potential friend relationship so as to meet in the physical world. We present the design and implementation of our proposed system and conduct real-world experiment to show how the place visit information and online friend relationship together produce a new offline social networking service.