The Perception layer in Internet of Things (IoT) architectures is responsible for connecting sensor nodes and data acquisition units such that sensing devices capture relevant data from the corresponding environment. Most IoT platforms are designed to transmit data at fixed time intervals, which is a disadvantage in the modern world dominated by rapid changes in the evolution of events. This paper addresses improvements performed on an IoT platform dedicated to critical applications (e.g., fire, air pollution). This novel approach assumes the use of two equations empirically determined to compute the time interval between successive transmissions depending on the detected event. A new method for communication technology selection (LoRaWAN, Wi-Fi or cellular) is implemented and the time interval between two successive transmissions is adjusted according to the occurring event. Comparisons were highlighted for each analyzed case. The proposed method proved to be suitable for critical scenarios or scenarios that can generate false-positive alarms, due to abnormal variations of parameters.
The applicability areas for sensor networks vary from industrial automation, environmental observation to medical domain [1]. As the quality of life has improved, the life expectancy also increased during the last years, fact that leads to an aging of the population. It is well known that elderly people need special treatment and resources due to their decreasing capacity of self-caring. It is, thus, desirable to increase the length of independent living for this category without depriving them from the known life environment and personal habits. Another possible application is the one of child care and monitoring in closed precincts. This paper illustrates the implementation steps of a sensor network used for discriminating between the presence of a human being and of an animal that may be useful in case of medical emergency situations. The design takes into account the main challenges that may occur such as achievement of not accurate results due to the fact that children are moving much more than an adult. The basic structure is designed using Arduino platform, sensors for distance measurements, for height determination as well as DHT22 temperature sensor and sensors for motion detection and takes into account cases of walking and standing subjects. Several configurations have been tested in order to improve the relative error for discrimination between children and pet entering a room.
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