International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This study presents and validates a method for achieving positioning accuracy comparable to standalone GPS (approximately 10 m) using a proprietary Sigfox WiFi positioning system that transmits only two MAC addresses per message. Due to Sigfox’s severe payload limitations, conventional positioning attempts often fail or yield significant errors. Our approach introduces continuous scanning on the device to dynamically collect up to four MAC addresses, paired with meticulous backend analysis and filtering of MAC address quality using a custom Here API Validation Tool. The proposed method effectively addresses these limitations, demonstrating a 100% location acquisition rate and a median positional error of 19 meters, making it highly effective for both stationary and dynamic mobile tracking.

When location was estimated using Sigfox’s Atlas Wi-Fi, the estimation error is several hundred meters. Meanwhile, when location was estimated using the Wi-Fi media access control (MAC) address and HERE’s API, the estimation error was ~10 m. In an Internet of Things device that obtains the Wi-Fi MAC address, the combination of exp32 and 100a was ~1/5th of the combination of Arduino R4 and Sigfox Shield.

In this study, the Sigfox network and the Internet were employed to develop an Internet of Things device equipped with temperature, humidity, barometric pressure, and other sensors to measure different physical data. In addition, we attempted to control Sigfox communication by employing AT commands using the Arduino UNOR4 Sigfox module, and social implementation of the proposed system.

The Internet of Things (IoT) has seamlessly integrated more than a billion intelligent, interconnected devices into our world today, and is expected to accommodate soon hundreds of billions more such devices. This imminent proliferation heralds a profound transformation that stands to influence the electronics industry and many other sectors. Furthermore, integrating blockchain technology into IoT can impart genuine trust in the collected data. Hence, our research endeavors to harness this potential by applying it to hydrogenrelated data, which are expected to be critical in realizing a carbon-neutral society.
Specifically, our attention is directed toward utilizing an initial blockchain system to securely store data obtained from IoT devices equipped with hydrogen sensors that can communicate through Sigfox technology.

We report the results of measuring carbon dioxide (CO2) concentrations 140 times a day in habitable spaces using Sigfox along with temperature, humidity, and atmospheric pressure measurements. We design and fabricate a printed circuit board to integrate the sensor with an Arduino processor. Using the developed sensor, we estimate that if one adult male performs light work for 1 h in a typical living room [3.17 m (width 3.17 m) × 7.4 m (length 7.4 m) × 2.6 m (height 2.6 m) = volume 61.4 m3], the carbon dioxide CO2 concentration will increase by approximately 200 ppm. In the future, the developed sensor could be used for detailed human behavior studies in the context of CO2 concentrations.

We achieved a CT(ozone concentration(ppm) X exposure time(min) )value of 60, which can inactivate coronavirus from 1/10 to 1/100 by operating for 24 h at an appropriate ozone concentration (less than 0.1 ppm) using feedback control in the previous report. In this system, the only way to reduce the ozone concentration is the decomposition reaction of ozone in the natural world, and it was not possible to cope with the rapid increase in ozone. In this study, we investigated the reduction of ozone concentration using hydrogen. As a result, it was clarified that the concentration of ozone rapidly decreased even with about 1/100 of hydrogen’s explosion limit (about 4%).

Using the Sigfox network, one of LPWA (Low Power Wide Area), and the Internet, we developed a temperature, humidity, pressure, and Internet of Things devices with different sensors to measure various physical data. It is much simpler to work with weather data when using OpenWeatherMap’s widely recognizable weather products. In this study, OpenWeatherMap’s meteorological data were captured every 15 minutes using are Sigfox communication, which is one of LPWA. These data were transferred to ThingSpeak using MATLAB analysis and time control of ThingSpeak.