The datafication of spatio-temporal city-wide events is one essential factor for smart management of the city. For this purpose, the combination of real-time event detection on edge sensor nodes mounted public vehicles, and event accumulation on a server is one realistic and efficient solution. We can analyze the accumulated data to understand complex phenomena occurring in entire the city. In this paper, we introduce a novel datafication procedure of city-wide events by sensor mounted garbage trucks and evaluated the preliminary implementation of event detection system on actual vehicle-mounted sensors.

This paper proposes a new method that makes it easy for us to construct a positioning model for device-free passive indoor localization by using model transfer techniques. With device-free passive indoor positioning, a wireless sensor network is used to detect the movement of a person based on the fact that RF signals transmitted between a transmitter and a receiver are affected by human movement. However, because device-free passive indoor positioning relies on machine learning techniques, we must collect labeled training data at many training points in an end user's environment. This paper proposes a method that transfers a signal strength model used for locating a person obtained in another environment (source environment) to the end user environment. With the transferred models, we can construct a positioning model for the end user environment inexpensively. Our evaluation showed that our method achieved almost the same positioning performance as a supervised method that requires labeled training data obtained in an end user's environment.

We have constructed a sensor network in a real hospital environment to develop a system that prevents medical accidents by monitoring nursing activities. The network has been carefully designed to be dependable for practical purposes even in a network-unready environment with considerations of safety and stability as well as the consistency of the obtained sensor data. This paper describes the design and implementation of our sensor network. Through an experiment of about one week, we confirmed that our design performs well in a practical environment and obtains consistent data among the sensors worn by nurses and installed in the environment. Since a hospital has strict limitations on the use of sensor network equipment, the design described in this paper provides a practical solution for the construction of sensor networks in many indoor applications

This study clarified that a robot tracking a user can influence user decision-making. Two types of robot were used and the robots tracked or did not track the user. A laboratory experiment (n = 118) tested differences in selection ratios corresponding to a color name recommended by the robots. Subjects selected the recommended color name significantly more often when the robot tracked subjects' faces than in the no-recommendation situation, however, when the robot did not track subjects' faces with recommendation, there was no significant difference. The physiological data also showed that the tracking condition generated higher levels of arousal than the non-tracking condition. This suggests that tracking the user's face influences user decision-making.