Han-Gyu Ko

SoIoT: Toward A User-Centric IoT-Based Service Framework

An emerging issue in urban computing environments is the seamless selection, composition, and delivery of user-centric services that run over what is known as the Internet of Things (IoT). This challenge is about enabling services actuated by IoT devices to be delivered spontaneously from the perspective of users. To accomplish this goal, we propose the Service-Oriented Internet of Things (SoIoT), a user-centric IoT-based service framework, which integrates services that utilize IoT resources in an urban computing environment. This framework provides a task-oriented computing approach that enables the composition of IoT-based services in a spontaneous manner to accomplish a user task. Tasks can also be recommended to users based on the available IoT resources in an environment and on the contextual knowledge that is represented and managed in social, spatial, and temporal aspects. These tasks are then bound to a set of service instances and performed in a distributed manner. This final composition ensures the Quality of Service (QoS) requirements of the tasks and is assigned to multiple client devices for the efficient utilization of IoT resources. We prove the practicality of our approach by showing a real-case service scenario implemented in our IoT-based test-bed as well as experimental results.

A community recommendation method based on social networks for web 2.0-based IPTV

Web 2.0-based IPTV is a new Internet Protocol Television (IPTV) infrastructure that allows users to participate in content creation and consumption through Web-based communities that are formed based on user interests. However, there are some limitations in making users actively participate in creating and utilizing communities. First, users need to explicitly create and manage their communities. In addition, it is difficult for users to identify and join communities that meet their needs. This paper proposes a method to identify and recommend potential IPTV communities for users by using their social relationships and preferences. The main goal of this method is to motivate users to actively participate in creating and sharing their contents through recommended communities. We have implemented a prototype of Web 2.0-based IPTV that allows users to share their contents and build relevant knowledge regarding the contents through blogs and Wiki-based communities.

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.

Adaptive Service Selection According to the Service Density in Multiple Qos Aspects

In task-oriented service computing, a users computing goal is modeled and represented as a task, which is composed of activities that are performed by accessing service instances in a local environment. The abstract service requirements specified in an activity of a task are resolved and bound to service instances dynamically in runtime. When there are many candidate services that provide similar capabilities for a task, it is essential to consider quality of service (QoS) such as response time, latency, and availability to determine which service instances to use. Finding a service composition that meets the optimal level of quality is a well-known NP-hard problem-the time complexity for task-level (global) optimization increases exponentially as the number of services and the number of quality attributes increase. Although it is possible to use a heuristic approach that shows a reasonable response time with a certain level of service quality, this strategy often fails when there are hard QoS constraints that need to be considered in the task level. In this paper, to overcome this limitation, we propose an adaptive method of selecting services based on the hardness of QoS constraints. The basic idea is to sample services that represent a specific quality-value range. The quality-value range of candidate services is divided into smaller sub-ranges in which representative services are sampled and evaluated. At this time, the size of the QoS sub-ranges is determined adaptably based on the hardness of the QoS constraints. In a QoS sub-range, candidate services may have a similar QoS value for a quality attribute. We calculate the utility of candidate services in a QoS sub-range and sample the highest utility service. This process of sampling services and evaluating their utility value is repeated until it makes a composite service that has the highest level of global utility for a task. Our experiment results show that the proposed approach effectively improves the success rate of service composition while achieving a certain level of global optimality and maintaining a reasonable level of performance. Our approach shows up to 80 percent improvement in success rate in comparison to the existing heuristic approaches.

Distributed Coordination of IoT-Based Services by Using a Graph Coloring Algorithm

As emerging paradigms such as service-oriented computing and ubiquitous computing become combined, end-users are now being provided with a myriad of services to utilize smart objects to achieve their goals. The most promising standard to utilize these services is WS-BPEL, which employs centralized coordination for simpler management of interaction and synchronization. However, centralized approaches suffer from scalability and heterogeneity issues as well as inefficiency, especially when the system is managed across different entities. Moreover, it is quite unrealistic to assume that one client device has permissions to access all available operations of smart objects and can also support different types of required communication interfaces. Hence, in this paper, we propose a novel distributed coordination scheme that helps end-users collaborate more efficiently to achieve their common goals. Unlike traditional distributed coordination methods, which are limited to static environments, the proposed scheme incorporates dynamic ubiquitous computing environments where requirements of tasks and available resources can be altered throughout task execution. Under the proposed scheme, mobile client devices are able to self-collaborate without a dedicated central server by spontaneously electing a task coordinator among them. The proposed scheme also deals with dynamic events such as the joining and leaving of users, clients and tasks, and supporting dynamic reallocation while keeping them transparent for end-users. Finally, the proposed scheme is evaluated through simulations with different numbers of services and client devices, showing improved results in performance optimality, assignment efficiency, and dependency coverage of composition.

A blog-centered IPTV environment for enhancing contents provision, consumption, and evolution

There have been some efforts to take advantages of the Web for the IPTV domain to overcome its limitations. As users become the center of the contents creation and distribution, motivating user participation is the key to the success of the Web-based IPTV. In this paper, we propose a new IPTV framework, called a blog-centered IPTV, where personal blogs are the firstclass entities that represent user interests in IPTV contents. An IPTV blog provides a user with a set of interfaces for finding, accessing and organizing IPTV contents based on their needs, and becomes an active entity to join communities and to participate in making community contents evolved. We have implemented a prototype of the blog-centered IPTV, and showed that users can easily find and access their desired contents and successfully build community-based contents.

An Adaptive Quality Level Selection Method for Efficient QoS-Aware Service Composition

In the task-oriented service computing framework where services are composed together to accomplish a task goal of a user, appropriate component services need to be dynamically selected and bound to the task. When there are many candidate services that provide similar functionality, it is essential to consider quality of services (QoS) such as response time, cost, availability, and reliability to decide which component services to use. Finding a service composite that meets the optimal quality is a well-known NP-hard problem because the time complexity for the global optimization increases exponentially as the number of services or the number of QoS attributes increases. Although there is a heuristic approach that shows a reasonable response time with a certain level of service quality, it often fails when the global QoS constraints become tight. In this paper, we propose an adaptive way of dividing quality levels where candidate services are sampled and their QoS values are evaluated. The range of a quality level is dynamically decided based on the distribution of candidate component services on each QoS attribute, and the tightness of the constraint requirement within a task. Evaluation results show that the proposed approach can successfully reduce the failure rate of service composition while keeping the computation time reasonably low and ensuring the QoS optimality of composite services.

Generation of semantic clouds based on linked data for efficient multimedia semantic annotation

The major drawback of existing semantic annotation methods is that they are not intuitive enough for users to easily resolve semantic ambiguities while associating semantic meaning to a chosen keyword. We have developed a semantic-cloud-based annotation scheme in which users can use semantic clouds as the primary interface for semantic annotation, and choose the most appropriate concept among the candidate semantic clouds. The most critical element of this semantic-cloud-based annotation scheme is the method of generating efficient semantic clouds that make users intuitively recognize candidate concepts to be annotated without having any semantic ambiguity. We propose a semantic cloud generation approach that locates essential points to start searching for relevant concepts in Linked Data and then iteratively analyze potential merges of different semantic data. We focus on reducing the complexity of handling a large amount of Linked Data by providing context sensitive traversal of such data. We demonstrate the quality of semantic clouds generated by the proposed approach with a case study.

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.

Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments

Collaborative filtering (CF)-based recommender systems can be used to deal with the complexity problem of users when they want to identify possible tasks on the fly and perform desired tasks by using various smart objects in Internet of Things (IoT) environments. However, in order to use CF-based recommender systems, users need to provide their feedbacks and there are usually more than one criterion considered when users choose an item. Although there have been studies of multi-criteria recommendations, existing approaches require multi-criteria ratings that are explicitly given by users. It is usually a burden for a user to provide more than one instance of feedback on an item; therefore, user feedback datasets are usually sparse when users are asked to provide multi-criteria ratings. Due to the sparsity of multi-criteria rating data, the similarity measurements used by the existing approaches may produce biased results, possibly leading to degradation of the recommendation accuracy. This problem becomes worse as the sparsity of a dataset increases. To alleviate the effects of the data-sparsity problem, and to take advantage of using multi-criteria ratings, we proposed a multi-criteria matrix localization and integration (MCMLI) approach for collaborative filtering in this paper. The main goal of MCMLI is to find cohesive user-item subgroups (CUISs) for each criterion from sparse data, and to predict users’ interests for each criterion in a more precise manner. The proposed approach is composed of three phases. At the first phase, a given user-item matrix is divided into a set of CUIS matrices, each of which is organized with correlated users and items for each criterion. MCMLI repeats this CUIS generation process until the generated subgroups cover all elements of the given user-item matrix. To generate prediction results for each criterion, MCMLI then predicts user ratings on new items for each CUIS and aggregates the prediction results to make recommendations to users. To enable personalized recommendations, during the aggregation process, each user’s preferences on multiple criteria are weighted differently according to the number of CUISs to which the user belongs. We demonstrate the effectiveness of our approach by conducting an experiment with real-world datasets from TripAdvisor and Yahoo! Movies. The experimental results show that MCMLI outperforms existing multi-criteria collaborative-filtering-based recommendation methods in terms of the recommendation accuracy. In addition, unlike the existing multi-criteria recommendation approaches, even when the sparsity level of a dataset increases, the recommendation accuracy of MCMLI does not decrease significantly.