Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task.  approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy.  The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm

Relative signal strength coverage optimization in indoor and outdoor wireless LAN environments

Fading and obstacles constitute major threats to effective quality of service (QoS) delivery in wireless local area network (WLAN) environments. In this contribution, we investigate the signal quality of indoor and outdoor WLANs over a defined coverage area. We present experimental analysis of case studies that will be useful for further research and validate the system’s performance in practice. Using an optimized form of the pathloss models, a simulation of the system is carried out over short and extended coverage. Simulation results show that signal quality could be effectively managed to improve the system’s performance for both indoor and outdoor environments in the presence of fading and other environmental factors.Facultad de Informátic

Coverage in WLAN with minimum number of access points

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The Adaptive Multi-Resolution Frequency-Domain ParFlow (MR-FDPF) Method for Indoor Radio Wave Propagation Simulation. Part I : Theory and Algorithms

This report presents the theoretical background and new developments of the multi-resolution frequency domain ParFlow (MR-FDPF) approach for the calculus or radio propagation in Indoor environments for centimetric waves. This method has been developed to face the need of a best understanding of Indoor propagation and to help the WiFi network planning task. Indeed, the development of a wireless design tool is based firstly on a radio propagation engine to predict accurately the radio coverage of access points, with a limited computational load. Usual approaches in the literature are based on either empiric modeling, deducted from measurements, or geometrical optic formalism leading to ray-tracing. While the former suffers a lake of accuracy, the later needs a trade-off between accuracy and computational load, often difficult to assess. The approach proposed herein is based on a finite element approach. Once the problem developed in the frequency domain, the linear system thus obtained is solved in two steps: a pre-processing step which consists in an adaptive multi-resolution (multi-grid) pre-conditioning and a propagation step. The second step computes the coverage of a point source with an up-and-down propagation through the binary tree associated with the multi-resolution description. This approach solves exactly the linear system but with a strongly reduced computational complexity when compared to the time domain approach. For example, a full AP coverage at a macroscopic resolution and for an environment of 1000x600 pixels (i.e. $6000m2$ at a $10cm$ resolution) lasts less than $200ms$

A Location Fingerprint Framework Towards Efficient Wireless Indoor Positioning Systems

Location of mobile computers, potentially indoors, is essential information to enable locationawareapplications in wireless pervasive computing. The popularity of wireless local area networks (WLANs) inside and around buildings makes positioning systems based on readily available received signal strength (RSS) from access points (APs) desirable. The fingerprinting technique associates location-dependent characteristics such as RSS values from multiple APs to a location (namely location fingerprint) and uses these characteristics to infer the location. The collection of RSS fingerprints from different locations are stored in a database called radio map, which is later used to compare to an observed RSS sample vector for estimating the MS's location. An important challenge for the location fingerprinting is how to efficiently collect fingerprintsand construct an effective radio map for different indoor environments. In addition, analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. In this dissertation, we provide a location fingerprint framework that will enable a construction of efficient wireless indoor systems. We develop a new analytical model that employs a proximity graph for predicting performance of indoor positioning systems based on location fingerprinting. The model approximatesprobability distribution of error distance given a RSS location fingerprint database and its associated statistics. This model also allows a system designer to perform analysis of the internal structure of location fingerprints. The analytical model is employed to identify and eliminate unnecessary location fingerprints stored in the radio map, thereby saving on computation while performing location estimation. Using the location fingerprint properties such as clustering is also shown to help reduce computational effort and create a more scalable model. Finally, by study actual measurement with the analytical results, a useful guideline for collecting fingerprints is given

Improvement of indoor environment signal reception using PLC-RF diversity techniques

D.Ing. (Electrical and Electronic Engineering)Abstract: Please refer to full text to view abstract