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project experiments [6]. Working in the same area is HP Laboratories of Cambridge
through an experiment, which consists in monitoring driver's physiologic reactions
during real-world driving situations under normal conditions. This kind of research
has the advantage of allowing the knowledge of implications given by a realistic
scenario and makes possible design a solution more directly applicable [5].
Crime detection is a new technology, which uses sensing computing for identifying
dangerous situations. In Dongguk University at Gyeongju, Korea, was proposed a
system capable of detecting crimes in real-time by analyzing human's emotions. To
facilitate this identification, wearable sensing devices should be attached to user's
underwear and the users should be monitored by CCTV cameras. Once crimes are
detected the system notify the appropriate entity and saves the video in database [7].
E-health can be described as an intersection between medical informatics, health
and business, which enables health assistance and services through the use of Internet
and related technologies [9]. In this area it is very important to detect stress situations
for the identification of diseases related to this emotion.
There are several studies and experiments that use IoT and sensor networks for
identify stress. Generally the system created involves three stages: experiment setup
for physiological sensing, signal preprocessing for the extraction of affective features
and affective recognition using a learning system [3].
Passive sensors are used to collect data from the users living environment, but in
alternative these kind of information can also be found using RFID tags, for measur-
ing many aspects of the biological state of a person [7].
Comparing to researches which were developed until this moment, our proposal
has the advantage of providing an integrated system, which uses a WSN, RFID tags
and GPS. Moreover, on the opposite of applications mentioned previously, our
proposal solution attempts to identify two dangerous situations: fear and illness. To
provide this detection of the danger it will be used a self-learning algorithm, which
will give a better performance to our application.
3
Proposal Solution
In this section will be presented the proposal solution of a system capable of detecting
when a child is in danger through the use of a wearable smart IoT device.
Three levels of specification of the proposed architecture compose the structure of
this system: the IoT architecture, the application architecture and the self-learning
algorithm.
On the basis of the system is an IoT architecture and its three layers: perception
layer, transportation layer and application layer. The perception layer includes RFID
security, WSNs security and any others and the transportation layer is the responsible
for the access network security, core network security and local network security. The
application layer is the application support layer composed by the IoT applications.
The application architecture specifies the components of the proposal solution and
the methods, which allow the system to validate the state of danger.
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