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A natural disaster is a consequence of a natural hazard, such as a tsunami, earthquake or volcanic eruption, affecting humans. In order to support emergency medical communication services in natural disaster areas where the telecommunications facility has been seriously damaged, an ad hoc communication network backbone should be build to support emergency medical services. Combinations of requirements need to be considered before deciding on the best option. In the present study we have proposed a Low Altitude Platform consisting of tethered balloons combined with Wireless Fidelity (WiFi) 802.11 technology. To confirm that the suggested network would satisfy the emergency medical service requirements, a communications experiment, including performance service measurement, was carried out.

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.

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