Byoungoh Kim

InterX: A service interoperability gateway for heterogeneous smart objects

Due to the advances in wireless networking and microelectronics, more and more smart objects are equipped with wireless networking as well as high computing capability. Heterogeneity of service protocols inhibits the interoperation among smart objects using different service protocols. Existing works overcome the heterogeneity by assuming that service protocols used are known in advance so that components necessary for interoperations can be deployed before runtime. This assumption prevents existing works from being applied to situations where a user wants to spontaneously configure her smart objects to interoperate with smart objects found nearby without (or with minimal) user intervention. In this paper, we propose a service interoperability gateway that discovers service protocols used in a given environment, finds smart objects using these protocols, and instantiates proxies for interoperability between smart objects.

Personal genie: a distributed framework for spontaneous interaction support with smart objects in a place

With advancements on computing devices and the integration of multiple communication interfaces, everyday objects become interconnected with each other through the Internet -- so called Internet of Things (IoT). As filled with these smart objects, many of public places are getting smarter and users utilize proper services by means of smart objects. While the services can be provided based on certain servers, its hard to expect that there are local servers for public places. In this paper, we present a distributed middleware framework for spontaneous interaction with smart objects, Personal Genie, which supports smart object discovery, selective context acquisition, situation inference and task suggestion, task composition, and distributed task execution with a smartphone as the center. With Personal Genie, heterogeneous smart objects in a public place can collaborate to interpret current situations and provide necessary services and information to users. As a proof of concept, we build up a smart seminar room as a testbed of Personal Genie and implement a series of tasks upon the testbed for evaluation and show performance assessments with example scenarios.

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.

An Adapter Chaining Scheme for Service Continuity in Ubiquitous Environments with Adapter Evaluation

A key feature of ubiquitous computing is service continuity which allows a user to transparently continue his task regardless of his movement. For service continuity, the underlying system needs to not only discover a service satisfying a users request, but also provide an interface differences resolution scheme if the interface of the service found is not the same as that of the service requested. For resolving interface mismatches, one of solutions is to use an interface adapter. The most serious problem in the interface adapter-based approach is the overhead of adapter generation. There are many research efforts about adapter generation load reduction and this paper focuses on an adapter chaining scheme to reduce the number of necessary adapters among different service interfaces. We propose a construction-time adaptation loss evaluation scheme and an adapter chain construction algorithm, which finds an adapter chain with minimal adaptation loss.

A Place-Aware Stereotypical Trust Supporting Scheme

Proliferation of smartphones enables interaction among nearby users anywhere, anytime. Despite that these interaction opportunities may provide benefit to users, they also involve risks since nearby users may not be known. In this paper, we propose a decentralized stereotypical trust model that supports variation in human behavior based on context changes, specifically place. We leverage previous studies that show how the place of interaction affects the level of trust of users and applied them to a stereotypical trust scheme. Our results show that the proposed scheme not only performs faster in terms of stereotypes creation time (average 200% faster) but also reduces the rate of regarding a malicious user as a trustworthy one (average 75%) when compared to existing approach.

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 Clustering Based Schema Matching Scheme for Improving Matching Correctness of Web Service Interfaces

An interface adapter is used to allow a client application to transparently work with a service available in the local environment by converting the interface of an originally requested service into that of the available service. To generate adapters for users, interface matching is the first job to find out all the possible method matching pairs between a source interface and a target interface. Existing method matching schemes compare the schemas of source and target interfaces but they have low recall and precision because they only consider signatures and names of interfaces. For solving these problems, we propose a cluster-based schema matching scheme. By clustering methods in terms of parameter similarity, the proposed scheme can match methods more precisely than existing schema matching schemes. Then, we detect incorrect matches based on the analysis of incorrect method matching pairs.

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.

SemanticRadar: AR-Based Pervasive Interaction Support via Semantic Communications

Augmented Reality AR overlays relevant virtual information onto a real world view and allows the user to interact and virtually manipulate surroundings. Since virtual information resides not only in a virtual space, but also in a physical space, users can be spontaneously given a number of opportunities for enriched interactions with their environments. In this paper, we propose an AR-based pervasive interaction support, SemanticRadar, which allows a user to spontaneously interact with smart objects through semantic communications, leveraging the placeness of a users current location.

Urban Radar: An Enabler for Place-Aware Spontaneous Interactions

With the advancements on mobile phones and the integration of multiple communication interfaces, interaction between users is no longer restricted to spaces with connectivity with the Internet but can happen anywhere, anytime. This has promoted the development of frameworks to enable the interaction of co-located users. One of the challenging points of this interaction is to find similar and trustable users to interact with. Existing works focus mostly on properties related to mobile users to find similar users in the surroundings. However, the features of the space in which the interaction is taking place are an important factor for recommendation since it provides hints about the most suitable type of interaction among co-located users. In this work we present a mediator application called Urban Radar, designed for supporting spontaneous interaction of co-located users taking into account the semantics of the place. Our evaluation shows that the mobile social application recommendation based on placeness either extremely or mostly satisfies 60% of users.