6. Results and Analysis
For sending out an alert, the value of network status is checked. If cell network is
available, then a call is dialed. In case there is no cell network but Wi-Fi network is available,
then a message is sent out with the user location. For the worst case scenario of no networks
being available, the alarm signals for help nearby.
The subsystem works as follows: Once a fall is detected, the network availability
(cellular or Wi-Fi) is checked. For displaying user location in map view, the location co-ordinates
are scanned and sent to one of map services using a Java Script Object Notation (JSON) query
to retrieve and detect the location. If there is no network available, then a recently cached
location is used and GPS-based positioning scanning is stopped to prevent battery drainage.
The model has been tested using fall detection-response subsystem from Helping Our People
Easily (HOPE).
The Structure consists of seven modules; four of them have several modules. When
applying the proposed model, firstly, phase one will evaluate the reliability of the entire system.
Since system assessing effort that results from phase one is moderate, phase two will evaluate
the reliability of each module. The model was implemented using Matlab Big Data and applied
on the Environmental video system Structure.
As a result, the connection degree of alert transmitter module is increased by
connecting call dialer and message transmitter modules to alert raiser module.
As a result, the structure can modify these modules in order to reduce entire system
assessing efforts. This is can be done by increasing the connection degree and decreasing the
relevance of the modules that required high assessing efforts. In addition, it shows the
assessing effort (TE) for each module that result from the logical reasoning system. Notice that,
three modules out of seven have high assessing effort (the highlighted modules: network
scanner, power source and alert transmitter module).The assessment results for Environmental
video system Structure list the connection degree and the relevance values for each phase.
The Structure is assessed again to evaluate the modification results. Another
modification, to increase the connection degree of power resource module is to separate the
charger module into different module since it does not have any connection with battery module
or any other external modules. By this modification, the relevance between call dialer and alert
switcher, and message transmitter and alert switcher are eliminated. Thus, need to reduce the
total relevance of alert transmitter module. The comparison between system connection degree,
relevance, dependency, and assessing efforts before (called system_B) and after (called
system_A) the structure modifications. SCoh increases after Structure modifications. In addition,
SCop and CDD is reduced by 14% and 21% respectively. These enhancements results in
reducing system assessing effort by 42%. The results are expressed the percentage values of
the assessing efforts improvement, where “-” means the decreasing of assessing efforts. The
system assessing effort is reduced by 42% since the assessing effort for power source and alert
transmitter is reduced by 67% and 35% respectively.
7. Conclusion and Future Work
A logical reasoning system was built to assess both Big Data and module reliability
according to reasoning rules. The model was implemented and applied on a case study. In this
paper, we proposed an assessment model to assess Big Data Structure of Internet of Things
(Structure Design Assessment Model (SDAM)) based on reliability rules. The results showed
that the model is applicable and efficient and it can improve the reliability efforts. The model
directs Big Data Structure of Internet of Things on how to improve Big Data Structure of Internet
of Things reliability. As future works, we propose an adjustment logical rules and membership
functions based on testable Structures.