Heesuk Son

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 task-oriented service personalization scheme for smart environments using reinforcement learning

Users want IoT environments to provide them with personalized support. These environments therefore need to be able to learn user preferences, such as what temperature the room should be or which lights should be turned on. We propose a novel system that separates the tasks of learning a users preferences and realizing them within the environment. The system is able to capture user preferences by reinterpreting the problem as an optimization problem and applying inverse reinforcement learning to it. The system is shown to be able to accurately extract simple preferences given a small number of user demonstrations. These preferences are then realized by actuates devices running reinforcement learning-based agents to provide an environment consistent with the learnt preferences, also in situations not included in any user demonstration.

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.

An MARL-Based Distributed Learning Scheme for Capturing User Preferences in a Smart Environment

Providing a personalized service to a user in a smart environment has been one of the key goals in the area of pervasive computing. The proliferation of individually developed smart devices in the name of Internet of Things opens up a possibility of providing personalized services to a user in an autonomous and distributed manner. As a users task often involves services supported by multiple devices, capturing a device-specific service preference is not enough to maximize a users comfort. In this paper, we propose a distributed learning scheme for capturing multiple device service preferences in a smart environment. We exploit multi-agent reinforcement learning (MARL) method where each smart device acts as a reinforcement learning agent to incrementally and cooperatively capture a user specific preference of a task. Experiments confirm that smart devices with the proposed scheme are able to capture multiple device service preferences from a small number of interactions with a user and an environment. Also, the proposed transfer learning method improves learning performance for a new task.

An adaptive IoT trust estimation scheme combining interaction history and stereotypical reputation

As IoT devices become prevalent in our daily lives, estimation of their trustworthiness plays an important role for privacy protection complementary to security solutions. Existing trust estimation solutions are based on SIoT whose full social network is not likely to be observable in a public space or do not reflect situation-dependent dynamism of trust. In this paper, we propose a new trust estimation scheme that computes a users trust value of an IoT device combining both the personal trust from the interaction history and non-personal stereotypical reputation from the general public.

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.

CASPRE: A context-aware standby power reduction scheme for household appliances

Many household appliances draw standby power, which can be reduced to save total energy consumption of a household. Smart plugs are proposed for effectively reducing standby power of an appliance by monitoring and cutting power when the appliance is no longer in use. However, the level of intelligence it leverages is minimal such that it still requires a direct manipulation by the user and does not make a use out of any other contexts. In this paper, we propose a context-aware standby power reduction scheme, CASPRE that reduces the total standby power of household appliances by controlling smart plugs through context reasoning. The proposed scheme leverages the notion of correlations that exist among appliances when conducting a specific task at home. An ongoing task can be inferred from contexts of multiple appliances and those that do not belong to the current task can be turned off. Experiments on real world power datasets show that the proposed scheme more effectively reduces total standby power than existing ones.

IoT Trust Estimation in an Unknown Place Using the Opinions of I-Sharing Friends

Advances of IoT have enabled users to use intelligent services from surrounding smart objects. As the needs of preventing the misusage of personal information increases, the estimation of the trustworthiness of interaction counterparts before interaction is important to prevent potential dangers. Existing trust estimation solutions are based on Social IoT, which is hard to adopt in reality. Another approach uses indirect interaction history to estimate trustworthiness, which is not efficient in an unknown place. In this paper, we propose a trust estimation scheme that allows a user to evaluate the trust value of target device in an unknown place, by leveraging I-sharing friends subjective experience. Evaluation results show that the proposed scheme decreases RMSE up to 2.5 times compared with the existing approach.

An intelligent SDP discovery scheme using knowledge-based adaptive probing

As Internet of Things advances, various smart objects and services permeate in our living environment. Due to their dynamic join/leave behaviors, service discovery protocols (SDPs) have been proposed. As new SDPs are introduced and legacy SDPs evolve, the incompatible islands of smart objects inevitably arise. To solve this issue, many research works are presented, but they cannot accommodate a new protocol or a new version of the legacy protocol. To solve this limitation, we propose an intelligent SDP discovery scheme based on knowledge-based adaptive probing. It is composed of two main building blocks, SDP knowledge base and adaptive probing procedure. In the case study, a user device successfully discovers a new SDP version by means of the proposed scheme.

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.