Detecting Visibility in Heterogeneous Simulated Environments for Positioning Purposes
In previous research, we elaborated positioning systems based on heterogeneous data, such as GNSS and Wi-Fi, in order to calculate a 3D geographical position of mobile equipments. The results were quite interesting and encouraged us to study a more automatic positioning system with a transparent migration between different environments and equipments, without implementing any additional infrastructure. The aim of the paper is to describe the simulation system through the following steps:
- Simulating a 3D environment with equipments using different technologies of mobile communication.
- Elaborating mobility models towards these environments with a transparent transition between indoors and outdoors.
- Developing and studying the concept of visibility of the equipments using different mathematical and physical methods.
- Applying the previous steps towards a global positioning model taking advantage of telecommunication networks.
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Proposed systems for positioning purposes are based essentially on the triangulation process, using GPS signals.
In outdoor environment, the precision is very good and allows a real time positioning. Neither, the characteristics of the environment must be taken in consideration. Environments with difficult “line of sight" don't allow a positioning reliability and precision. These facts contribute to enhance the motivation of scientific communities to search new solutions for positioning systems.
Systems based on inertial sensor, WiFi systems or
Approaches and methods
The term “visibility" means “detection of equipments for a given technology" in our approaches. The visibility is thus defined by different types of methods. For instance, the visibility of two equipments is achieved
if they are in radio range. The coverage area could differ, according to the real environment.
if the distance between them is lower than a given distance. This distance is calculated using the Friis Formula, depending on the environment.
if the signal strength received is inferior to a predetermined threshold.
by studying the time of signal arrival.
if the number of hops between them is inferior to a threshold.
Today, equipments include multiple sensors, enabling them connections to different kinds of networks (the IPhone is a typical example). Our approach aims at evaluating the possibility to use these multiple data for positioning purposes. The proposed system was simulated with “Matlab". The paper also gives details on the 3D description of our building and its environment. Then, we developed simulating modules for the different technologies: GPS, Wi-Fi, GSM and Bluetooth. These modules simulate the interactions between networks. The figure below presents the organization chart of the system.
In addition, we implemented different types of mobility models. Several 3D trajectories were designed in order to provide possible realistic situations of mobility. Three of them are described in the paper, using three mobility models: Gaussian, Sinusoidal and Parabolic. Note that the most common model is the Gaussian mobility one. However, it does not take into consideration the 3D changes. For our purposes, we thus elaborated an enhanced 3D Gaussian Mobility Model, fully described in the paper. This step also requires the preparation of different possible paths within buildings and the outdoor environment, simulating user mobility.
Then, we studied the detection of equipments along the paths previously described. Mobile terminals wishing to estimate their positions must then establish a “visibility data collection". This collection is in the form of a database including all the equipments “seen" by the mobile terminal, as well as their attributes. The collection of the data is elaborated for every equipment, in our environment. The final database is composed by all this information. This step is necessary in order to establish a 3D representation of the geographic relations between equipments.
Results and Future works
Simulation results are provided showing the impact of both the visibility pattern and the mobility model chosen on the global connectivity of a mobile terminal. This connectivity will be the foundation of the next step of the complete positioning model under development. Our future works are thus oriented towards the design of this second step of an automatic system, consisting in carrying out positioning algorithms and computations. Note that experimental data are also provided in order to discuss the validity of the various models (visibility and mobility).
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