Map data representation for indoor navigation - a design framework towards a construction of indoor map

A map is a basic component used in a part of navigation in everyday life, which helps people to find information regarding locations, landmarks, and routes. By GPS and online service map e.g. Google maps, navigating outdoors is easier. Inside buildings, however, navigating would not be so easy due to natural characteristics and limitations of GPS, which has led to the creations of indoor navigation system. Even though the indoor navigation systems have been developed for long time, there are still some limitation in accuracy, reliability and indoor spatial information. Navigating inside without indoor spatial information would be a challenge for the users. Regarding the indoor spatial information, a research question has been drawn on finding an appropriate framework towards map data representation of an indoor public spaces and buildings in order to promote indoor navigation for people, robotics, and autonomous systems. This paper has purposed a list of factors and components used towards the design framework for map data representation of indoor public spaces and buildings. The framework, in this paper, has been presented as a form of a multiple-layered model, which each layer designed for a different propose, with object and information classifications

PINSPOT: An oPen platform for INtelligent context-baSed Indoor POsiTioning

This work proposes PINSPOT; an open-access platform for collecting and sharing of context, algorithms and results in the cutting-edge area of indoor positioning. It is envisioned that this framework will become reference point for knowledge exchange which will bring the research community even closer and potentially enhance collaboration towards more effective and efficient creation of indoor positioning-related knowledge and innovation. Specifically, this platform facilitates the collection of sensor data useful for indoor positioning experimentation, the development of novel, self-learning, indoor positioning algorithms, as well as the enhancement and testing of existing ones and the dissemination and sharing of the proposed algorithms along with their configuration, the data used, and with their results

Indoor positioning of shoppers using a network of bluetooth low energy beacons

In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a “nearest-beacon” approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie

An Open Platform for Studying and Testing Context-Aware Indoor Positioning Algorithms

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, our proposal features novelties related to the collection and use of additional context data. The platform is realized in the form of a mobile client, currently implemented on Android. It enables manual collection of radio-maps—i.e. fingerprints of Wi-Fi signals—while also allowing for amending the fingerprints with various context data which could help improve the accuracy of positioning algorithms. While this is a research-in-progress platform, an initial experiment was carried out and its results were used to justify its applicability and relevance

INDOOR LOCALIZATION AND TRACKING: METHODS, TECHNOLOGIES AND RESEARCH CHALLENGES

The paper presents a comprehensive survey of contemporary methods, technologies and systems for localization and tracking of moving objects in indoor environment and gives their comparison according to various criteria, such as accuracy, privacy, scalability and type of location data. Some representative examples of indoor LBS applications available on the market are presented that are based on reviewed localization technologies. The prominent research directions in this domain are categorized and discussed

Indoor location identification technologies for real-time IoT-based applications: an inclusive survey

YesThe advent of the Internet of Things has witnessed tremendous success in the application of wireless sensor networks and ubiquitous computing for diverse smart-based applications. The developed systems operate under different technologies using different methods to achieve their targeted goals. In this treatise, we carried out an inclusive survey on key indoor technologies and techniques, with to view to explore their various benefits, limitations, and areas for improvement. The mathematical formulation for simple localization problems is also presented. In addition, an empirical evaluation of the performance of these indoor technologies is carried out using a common generic metric of scalability, accuracy, complexity, robustness, energy-efficiency, cost and reliability. An empirical evaluation of performance of different RF-based technologies establishes the viability of Wi-Fi, RFID, UWB, Wi-Fi, Bluetooth, ZigBee, and Light over other indoor technologies for reliable IoT-based applications. Furthermore, the survey advocates hybridization of technologies as an effective approach to achieve reliable IoT-based indoor systems. The findings of the survey could be useful in the selection of appropriate indoor technologies for the development of reliable real-time indoor applications. The study could also be used as a reliable source for literature referencing on the subject of indoor location identification.Supported in part by the Tertiary Education Trust Fund of the Federal Government of Nigeria, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement H2020-MSCA-ITN-2016 SECRET-72242