THE GLOBAL OPTIMAL PLACEMENT OF BLE BEACON FOR LOCALIZATION BASED ON INDOOR MAP

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

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI

We present a low complexity experimental RF-based indoor localization system based on the collection and processing of WiFi RSSI signals and processing using a RSS-based multi-lateration algorithm to determine a robotic mobile node's location. We use a real indoor wireless testbed called w-iLab.t that is deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this testbed is that it provides tools and interfaces using Global Environment for Network Innovations (GENI) project to easily create reproducible wireless network experiments in a controlled environment. We provide a low complexity algorithm to estimate the location of the mobile robots in the indoor environment. In addition, we provide a comparison between some of our collected measurements with their corresponding location estimation and the actual robot location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Enhancing Indoor Localisation: a Bluetooth Low Energy (BLE) Beacon Placement approach

Indoor location-based services have become increasingly vital in various sectors, including industries, healthcare, airports, and crowded infrastructures, facilitating asset tracking and user navigation. This project addresses the critical challenge of optimising beacon placement for indoor location, employing Bluetooth technology as the communication protocol. The significance of this research lies in the effi ciency and accuracy that an optimised beacon layout can provide, enhancing the effectiveness of indoor positioning systems. The algorithm developed takes into con sideration materials attenuation, coverage and Line of Sight (LOS) conditions to optimise its layouts. Experimental validation of the algorithm’s performance was conducted by comparing two beacon layouts: one optimised by the algorithm and the other manually arranged by individuals with empirical knowledge in the field. The experiment considered three distinct positions within the schematic, allowing for a comprehensive assessment of the optimised layout’s superior performance. The results of this research offer insights into the potential of the algorithm to revolu tionise indoor location services, providing a more reliable and cost-effective solution for a multitude of applications.Os serviços de localização em ambientes internos tornaram-se cada vez mais essenciais em vários setores, incluindo indústrias, cuidados de saúde, aeroportos e infraestruturas movimentadas, facilitando o rastreamento de objetos e a navegação de utilizadores. Este projeto aborda o desafio crítico da otimização da colocação de beacons para localização em ambientes internos, utilizando a tecnologia Bluetooth como protocolo de comunicação. A importância desta pesquisa reside na eficiência e precisão que uma disposição otimizada de beacons pode proporcionar, melhorando a eficácia de sistemas de posicionamento em ambientes internos. O algoritmo desenvolvido leva em consideração a atenuação de materiais, a cobertura e as condições de visão direta para otimizar as suas disposições. A validação experimental do desempenho do algoritmo foi realizada ao comparar duas disposições de beacons: uma otimizada pelo algoritmo e outra organizada manualmente por indivíduos com conhecimento empírico na área. A experiência considerou três posições distintas no esquema, permitindo uma avaliação abrangente do desempenho superior da disposição otimizada. Os resultados desta pesquisa oferecem descobertas importantes sobre o potencial do algoritmo para revolucionar os serviços de localização em ambientes internos, proporcionando uma solução mais confiável e econômica para uma variedade de aplicações

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

iBeacon-based indoor positioning system: from theory to practical deployment

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable