Indoor location services and context-sensitive applications in wireless networks

Abstract

The present research was conducted at the Budapest University of Technology in the field of indoor location using radio waves of Wi-Fi networks with a focus on practical application issues. Our goal was to enhance and combine existing algorithms and create an implementation that is efficient enough to enable real-time operation in 3D space in multi-level office environments while retaining the accuracy of more complex systems and allowing the addition of valuable context-sensitive features.The proposed solution is based on proven empirical propagation models, with the ability to augment and refine with surveyed radio fingerprint data. The results of the location algorithm are combined with heuristic probability distributions layered over the floor plan, thus incorporating available sensor data (accelerometers and magnetometers found in modern devices) and floor plans. The positioning system is used by an indoor navigation software developed for smartphones and complemented by a database of annotated entities (persons, devices, areas and points of interest) to allow the inference of location-dependent, contextsensitive information through an API provided for upper layer applications.This paper primarily focuses on the practical experience accumulated during the development of the positioning algorithm, as well as the possibilities opened by having a dynamic view of all the attributes assigned to persons either directly or indirectly via their locations or other external data sources. The implementation heavily relies on the Spatial extension of the Oracle Database system, which provides geometric data types and operations. The benefits of the employment of such a system are going to be presented in the current article.

3-Dimensional Indoor Positioning System based on WI-FI Received Signal Strength using Greedy Algorithm and Parallel Resilient Propagation

“…Gansemer, Hakobyan, Puschel & Grosmann, (2009) [11] , in their experiment used ISO Line and Euclidean algorithm for floor determination and they claimed that using Euclidean distance algorithm they determined exact floor with 100% accuracy. Schulcz, Varga & Tóth (2010) [10] , developed IPS by combining empirical propagation model with fingerprinting, they also utilized accelerometer and magnetometer and achieved an accuracy about 3.09m. After around three years, Li et al (2013) [8] , in their study integrated WiFi and GSM technologies and using K-nearest neighbor algorithm they were succeeded in correct room prediction with an accuracy of 72%.…”