System Simulation

The simulation of the WSN is an essential step in its design, prior to its deployment, and enables the isolation of several aspects inherent to its continuous and optimal working. Of great complexity in this simulation, is the isolation of single aspects based on configurable parameters that will enable the identification of the problems in need of fixing and to take appropriate corrective actions. Although the simulation of the WSN is not within the focus of this research, the system developed includes the concept of a virtual control room to visualize the different WSN entities and their configuration and behaviour.This system in relation with the proposed case study provides some simulation tools enabling:

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 Visualisation of the WSN entities,

 A navigation path of the building with decision information control,

 The homogenous data capture,

 A sensor node cluster alarm reaction model, and

 The display of evacuation paths.

5.5.1. Virtual Control Room

The visualisation of the WSN entities and their status are displayed as the result of the data information fusion process, involving the activation of intelligent agents interacting with each other, when their corresponding services are automatically invoked during the deployment of the WSN and the execution of context applications. Supporting the monitoring of the domain activity, intelligent agents composing the invoked services are activated automatically when event conditions are identified and an automated instantaneous reaction is performed. An example of monitoring of the WSN architecture in the virtual control room is illustrated in Figure 5.25.

Figure 5.25: Example of screen from the virtual control room

The monitoring of the activity domain is based on a multiple screen-capable web-based service application aimed at providing an overview of the system working, highlighting possible defects in different areas of WSN control with a detailed mapping of the network entities, events, and corresponding domain applications to enable, when possible, rapid fault analysis.

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5.5.2. Building Navigation Path

Building a navigation path, which is a path finding a way between the different rooms of a building, can be used to support:

 The access control of people in a building

 The definition of evacuation decision variables for:

o The identification of evacuation exits inside and outside the building, and o The calculation of the theoretical time required to evacuate people from a

room.

 The risk assessment required for emergency preparedness to ensure that the number of exits and their dimensions are ample to evacuate the population limit of a building.

This information is elaborated from:

 The wall segment of the rooms, particularly the door and windows segments for the identification of exits inside and outside the building, and

 The evacuation model for the calculation of the theoretical time, using in combination the room occupancy model and the evacuation rate.

The evacuation theoretical time can be improved by incorporating the position of each person in the building and the simulated time required for them to leave the building. This is illustrated in the decision tool shown in the next section.

5.5.3. Evacuation Simulation

The RFID technology used in this research work to support the localisation and tracking of people and objects can be used to assist people in their movements inside the building by identifying their physical conditionsand associated actions which are essential, for example, in the case of emergency evacuation.

Enhanced messaging devices can be incorporated in their tags along with sensors, and elaborated information also stored to personalise the emergency evacuation.

The evacuation path is defined as the linear distance from:

 The person localisation to the exit of the attended room, and

 The door to door of the next rooms as suggested in the building navigation path shown in Figure 5.26.

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Figure 5.26: Building navigation path

The evacuation path finding takes into account the presence of objects in the path, and collision detection and path re-planning is performed when required. However, the evacuation time has not integrated the waiting time at the collision nodes which are the evacuation points (doors or windows) between the different rooms. In the evacuation data displayed in Figure 5.27, outside the building is referred as Room 0.

Figure 5.27: Building evacuation simulation data

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5.5.4 Sensor Node Cluster Alarm Reaction Model

A sensor node cluster alarm reaction model has been developed to demonstrate the coordination of the different detectors forming the cluster in showing the progression of the alarm along the fire propagation.

This model is based on calculating the fire proximity to the detector location which sets its alarm on when this distance becomes equal or smaller than the sensing distance. As the fire propagates, the individual distance to each detector in the cluster and to other surrounding detectors increases by integrating the effect of the two following parameters:

 Fire expansion or propagation, and

 Temperature rise.

The data resulting from this alarm simulation is twofold:

 The predictive data given by the fire simulation model, and

 The data captured by the WSN from the different detectors.

The predictive data shown in Figure 5.28 is composed of:

Figure 5.28: Predictive data for fire alarm simulation

 DIST: Distance from fire to detector,

 FRDT: Fire reaching detector time,

 EFTD: Expected fire temperature at detector,

 DLRD: Distance left to reach detector and

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 TLTA: Time left to alarm.

The data captured by the WSN from the detectors is detailed in the next section.

5.5.5 WSN Data Capture Simulation

The data captured by the WSN, which is heterogeneous as it corresponds to the output of different homogeneous and heterogeneous devices which may have different configurations, is shown in Figure 5.29. In the case study supporting the implementation of the system prototype developed in this research, a data reading from a device is composed of nine values composed of:

 Device identification

 Six values allocated to sensors data

 Two values allocated to RFID data (x and y coordinates).

Figure 5.29: WSN generated simulation data

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5.6 Summary

The implementation of the system prototype has been limited to the first two steps of the sensors and WSN data fusion involving data capture and processing. Although subsequent steps concerned by the support design for active nodes performing in-networking activities, and information fusion involving intelligent agents can take place only after the configuration and deployment WSN in real world, the case study has demonstrated the practicality of the conceptual generic design framework for hybrid intelligent decision support systems, illustrating the integration of the three major management components for data, knowledge and models. The integration of WSN and RFID is also illustrated in the design support and use of hybrid smart devices grouping WSN and RFID capabilities that enable functional integration of knowledge processes.

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