Jang-Ho Choi

SDIF: Social device interaction framework for encounter and play in smart home service

In this paper, a framework for the mutual interaction of smart devices used in a smart home service is proposed by utilizing the concept of device sociality to minimize human interventions. The framework makes easy device collaboration possible by providing social relations among devices when they encounter each other. To measure the effect of the reduction in human intervention during the process of configuring the mutual interactions of such devices, a zero configuration index of the mutual interaction processes among smart devices was adopted. To show its feasibility in terms of minimizing human intervention, the proposed method was compared with another traditional device interaction protocol for a file and screen sharing application between two smart devices.

D2D distance measurement using Kalman filter algorithm for distance-based service in an office environment

The device-to-device (D2D) distance-based service is affected by distance between the D2D devices. Thus, the wireless distance measurement between devices is an indispensable technology in D2D applications. When mobile devices use the distance between D2D devices in the D2D application, the distance should be measured easily and accurately. Several methods can be used to measure the distance between D2D devices. The RSSI system can be used to estimate the D2D distance easily and inexpensively because most D2D devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. We tested the relationship between distance and a Bluetooth RSSI to look into the inaccuracy of the RSSI in an office environment. Through the results of this test, we realize that the D2D distance is hard to be classified due to the inaccuracy of Bluetooth RSSI. Therefore, filtering algorithm should be applied to reduce the inaccuracy of the Bluetooth RSSI. Nowadays, Kalman filter is widely used in many research fields and shows good performance. Thus, we use the Kalman filter to increase the accuracy of Bluetooth RSSI. To evaluate the effectiveness of the Kalman filter algorithm, we tested the D2D distance measurement in an office environment. The result of this test indicates that the Kalman filter reduces the inaccuracy of Bluetooth RSSI significantly. In addition, we describe wireless body area network (WBAN) region classification using Bluetooth RSSI and Kalman filter in an office environment.

Towards zero-configuration in device collaboration using device sociality

Collaboration of smart devices often requires troublesome configuration and management, involving human interventions. In order to reduce these burdens, smart devices should be able to configure and manage themselves in device collaboration, self-understanding high-level information such as social relationship between the owners. We propose a concept called, device sociality, which aims automatic configurations in device collaboration via self-constructing social relationships. Device sociality describes social relationships of devices, which can be identified by analyzing machine-level communication data and human social data. We conducted an initial experiment to show that devices can self-identify their social relationship with minimum human intervention. We verified the result with user survey, providing that devices are able to collaborate autonomously with device sociality.

Investigating correlations between human social relationships and online communications

Online communications not only provide instantaneous and inexpensive mean for socialization, but also present an opportunity to understand human relationship. In this paper, we investigated correlation between human social relationship and online communications and modelled personal and business affinity as a linear combination of online interactions. We discovered that personal and business affinities have different dominant predictive variables. The discovered variables not only had a strong linear relationship to their affinity, but also were statistically significant.

Enabling intelligent device communication using device sociality

Although the proliferation of the Internet of Things has opened up a new era of innovation, its utilization is often difficult, requiring human intervention. In this paper, we introduce a concept called device sociality which enables more intelligent device communication. By monitoring and analyzing their interactions, devices can discover unique relationships toward other objects. Devices may mimic the human socialization process in accumulating social relationship from exchanging services and resources. With the social relationships formed, devices are able to discover and identify trustworthy peers for their tasks and collaborations autonomously. We have implemented the device sociality in smart education domain and verified its feasibility by demonstrating the system in an education expo.

A bridge structure of device sociality framework for smart home service

This paper proposed a bridge structure of the device sociality framework for smart home service. The framework makes easy device collaboration possible by giving the social relation among devices. In this paper, we proposed a kind of bridge structure to extend the framework easily to home network environment for smart home service. To show the feasibility of the proposed method, we applied the proposed bridge approach of the framework to the UPnP AV architecture service among smart devices.

A Layered Features Analysis in Smart Farm Environments

Calculating or predicting the harvest yield of crops in agriculture has been one of its most important techniques. Smart farms, with the power of IoT, provide invaluable information such as real-time environment data such as temperature, humidity, etc. However, there are situations where not all data can be sent over the internet due to private reasons. In this paper, we propose a scalable data analysis framework: the edges preprocess and analyze the private data and send the results to server, and the server gathers and accumulates the results to estimate and predict the total harvest yield. Based on the results obtained from a real tomato farm, error rate is comparable to the one performed at the server only, but the number of features is reduced significantly.

A Hierarchically Switching Analysis Architecture with Streaming Data from IoT (Internet of Things) Sensors

In this paper, we proposed a hierarchical analysis architecture for IoT streaming data by switching computing load between an analysis server and edge devices dynamically in order to reduce the resource usage of the analysis server. The hierarchically distributed switching analysis system can analyze faster and more IoT streaming data with the same computing capability. To show the feasibility of the proposed method, we applied the proposed method to the prediction problem of energy consumption of a simulated BEMS (Building Energy Management System) which is based on real BEMS data.

Real-time distance awareness algorithm based on wireless signal strength and accelerometer sensor of smart device in an indoor environment

When mobile devices use the distance between devices in an application, the distance should be measured easily and accurately. The RSSI system can be used to estimate the distance easily and inexpensively because most devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. Therefore, we propose distance awareness algorithm based on the RSSI and accelerometer sensor suitable for real-time distance estimation in an indoor environment. To evaluate the effectiveness of the proposed algorithm, we tested the distance measurement in an indoor hall environment. The results of this test indicate that the proposed algorithm does not only reduces the inaccuracy of Bluetooth RSSI significantly, but also estimates real-time distance more accurately using an accelerometer sensor.