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Agile environmental monitoring is a novel style of environmental-monitoring development that lets you rapidly develop sensor devices and monitoring systems through trial and error during continuous sensing. The authors developed three environmental-monitoring systems and adapted them in situ based on actual field requirements. This article is part of a special issue on smart cities.

In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless 3D-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.

This paper compares the effect of a robot's and on-screen agent's recommendations on human decision-making using a quantitative evaluation method. We are interested in whether a robot's physical body produces some differences in the effect or not. Previous research investigated the advantage of a physical body; however, the advantage was not clarified quantitatively and there was not enough evidence to give the results credibility. Our method based on quantitative evaluation clarifies the effect of a robot's and on-screen agent's behavior on user decision-making. Comparing a robot's behavior with an on-screen agent's, we show that the degree of the effect firmly depends on the interaction environment and that geometrical consistency between the interaction environment and embodied social agents (ESAs), which include robots and on-screen agents, is important in the recommendation situation.