Abstract
Abstract. Anchor Nodes in a localization system obviously play a crucial role in determining the system’s quality. Their placement directly affects the localization accuracy and their number directly impacts the total cost of the system. Nowadays, the deployment of Bluetooth nodes in industry generally relies on the experience knowledge of engineers and the cost of positioning beacon does not considered the global level. In this paper, we put forward a method to extract the number and location of BLE beacon automatically and ensure a high positioning accuracy of the indoor positioning system based the rules of indoor positioning, which use all kinds of space objects and structure characteristics of indoor map. The triangulation method was selected to study the global optimal placement of BLE beacon for localization based on indoor map. The impacts and requirements of BLE beacon placement were systematic analysed from the triangulation positioning method, indoor positioning environment and indoor user distribution characteristics. According to the characteristics of indoor environment structure and user distribution, we built an optimization model of BLE beacon placement method based on genetic algorithm which can generate the number and the location of BLE beacon. At last, the Bluetooth indoor positioning prototype system is developed to compare the experience method deployment scheme and the global optimization deployment scheme in the real indoor positioning environment.
Highlights
In recent years, people's demand for location based services has gradually extended from outdoor to indoor
With the rapid development of the Internet of Things technology and indoor positioning technology, some scholars have been conducted research work on indoor positioning nodes deployment, analysed the influencing factors of wireless sensor node deployment, and achieved certain results. Various optimization methods such as PSO algorithm, simulated annealing algorithm and genetic algorithm are gradually applied in the anchor nodes deployment process
In the existing research results, the analysis of the influencing factors of indoor positioning anchor nodes deployment is not systematic enough, and the relationship between positioning error and node deployment is not comprehensively analysed; the ruled rectangular or cuboid area is generally used in the anchor nodes deployment process, ignoring the complex structural features of indoor space; the anchor nodes deployment process does not take into account the active area and distribution characteristics of indoor pedestrians; the experimental analysis of the existing research is mostly realized by simulation and simulation implementation, and no verification and application are carried out in the real environment
Summary
People's demand for location based services has gradually extended from outdoor to indoor. The indoor environment is complicated, the spatial layout and the topological relationship are variable, and the implementation of the anchor nodes deployment by the empirical method has low efficiency Under this condition, empirical method cannot ensure effective positioning accuracy in each region, and does not consider the cost of positioning the anchor nodes at a global level. With the rapid development of the Internet of Things technology and indoor positioning technology, some scholars have been conducted research work on indoor positioning nodes deployment, analysed the influencing factors of wireless sensor node deployment, and achieved certain results Various optimization methods such as PSO algorithm, simulated annealing algorithm and genetic algorithm are gradually applied in the anchor nodes deployment process. This paper chooses the most common triangulation method at present, based on two-dimensional indoor map, quantitatively analyses the influencing factors of Bluetooth anchor nodes deployment in depth, determines the optimal deployment distance according to the positioning rules and Bluetooth signal propagation characteristics, and fully consider the structural characteristics of indoor space, combine the distribution rules of indoor pedestrians, and build a global optimization model for Bluetooth anchor nodes deployment with the goal of least using Bluetooth at the end
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