Ensuring the Correctness and Well Modeling of Intelligent HMS
367
– Mainak et al. [13] designs an agent based model which predict the response time
of emergency service by taking into consideration the characteristics of road segments and driving behaviour of emergency vehicle drivers. First, they collecting
real time driving data by Fire emergency service of Allahabad city using GPS logger HOLUX M1000C. Then, they analyze collected data in GIS along with road
network, population density and land use data. Based on the analysis results, they
model a fire emergency vehicle (FEV) service. For validation, they compare the theoretical response time with the measured one by simulating scenarios that have 80%
matching segments.
– Hussein et al. [7] propose a coordination emergency responses framework using
agent-based modeling. The main components of this model are Emergency
Response Services, Coordination Unit, MCI, Command and Control Center, and
Agent Based Simulation. In case of incident, the MCI sends an aid request to the
command and control center which transfers all information needed to emergency
response services. Next, all resource information are gathered and updated as necessary by this unit. Finally, the resulting plan from the coordination unit will be sent
to the agent based simulation, which used to simulate emergency response tasks in
real environments, and identify the best coordination mechanism plan to achieve the
best response time.
– Catarinucci et al. [1] propose a “Smart Hospital System” (SHS) for automatic monitoring and tracking of patients, personnel, and biomedical devices. It composes of a
“Hybrid Sensing Network” (HSN) that collects both environmental conditions and
patients’ physiological parameters as well as the IoT Smart Gateway that controls
the overall SHS behavior. The user interfaces builtin RESTful services which allow
user to communicate with the HSN through the 2-way Proxy.
– Molano et al. [17] propose an architecture of IoT applied to the industry. First, they
present a metamodel that generates industrial cases by extending cyber physical
systems to cover covers sensors and actuators to monitor manufacturing. However,
safety of data and system accuracy, standardization of technology and interoperability of systems within actual deployments are not considered.
– Cristian et al. [4] review the artificial intelligence-IoT fusion with a focus on four
important fields. They presents AI basics and the general concepts of computer
vision and Fuzzy Logic, and their link with IoT. Besides, they present natural language processing to facilitate the humain-machine interaction.
– Espada [3] proposes a model for constructing and interpreting digital objects in
IoT systems which can eliminate the management of pre-configuration and requirements. The model covers the integration and communication of digital objects, applications, devices and users.
Based on the discussed literature, few of them rely on the satisfiability analysis and
the stand modeling language to ensure the robustness of the developed solution.
3 HMS Modeling
This section describes the structural and behavioral diagrams of HMS proposed system
through UML use case, class, and sequence diagrams. Figure 1 shows the main actors
367
– Mainak et al. [13] designs an agent based model which predict the response time
of emergency service by taking into consideration the characteristics of road segments and driving behaviour of emergency vehicle drivers. First, they collecting
real time driving data by Fire emergency service of Allahabad city using GPS logger HOLUX M1000C. Then, they analyze collected data in GIS along with road
network, population density and land use data. Based on the analysis results, they
model a fire emergency vehicle (FEV) service. For validation, they compare the theoretical response time with the measured one by simulating scenarios that have 80%
matching segments.
– Hussein et al. [7] propose a coordination emergency responses framework using
agent-based modeling. The main components of this model are Emergency
Response Services, Coordination Unit, MCI, Command and Control Center, and
Agent Based Simulation. In case of incident, the MCI sends an aid request to the
command and control center which transfers all information needed to emergency
response services. Next, all resource information are gathered and updated as necessary by this unit. Finally, the resulting plan from the coordination unit will be sent
to the agent based simulation, which used to simulate emergency response tasks in
real environments, and identify the best coordination mechanism plan to achieve the
best response time.
– Catarinucci et al. [1] propose a “Smart Hospital System” (SHS) for automatic monitoring and tracking of patients, personnel, and biomedical devices. It composes of a
“Hybrid Sensing Network” (HSN) that collects both environmental conditions and
patients’ physiological parameters as well as the IoT Smart Gateway that controls
the overall SHS behavior. The user interfaces builtin RESTful services which allow
user to communicate with the HSN through the 2-way Proxy.
– Molano et al. [17] propose an architecture of IoT applied to the industry. First, they
present a metamodel that generates industrial cases by extending cyber physical
systems to cover covers sensors and actuators to monitor manufacturing. However,
safety of data and system accuracy, standardization of technology and interoperability of systems within actual deployments are not considered.
– Cristian et al. [4] review the artificial intelligence-IoT fusion with a focus on four
important fields. They presents AI basics and the general concepts of computer
vision and Fuzzy Logic, and their link with IoT. Besides, they present natural language processing to facilitate the humain-machine interaction.
– Espada [3] proposes a model for constructing and interpreting digital objects in
IoT systems which can eliminate the management of pre-configuration and requirements. The model covers the integration and communication of digital objects, applications, devices and users.
Based on the discussed literature, few of them rely on the satisfiability analysis and
the stand modeling language to ensure the robustness of the developed solution.
3 HMS Modeling
This section describes the structural and behavioral diagrams of HMS proposed system
through UML use case, class, and sequence diagrams. Figure 1 shows the main actors