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

A Hidden Environment Model for Constructing Indoor Radio Maps

Constructing indoor radio maps plays an important role in many services and applications such as wireless base station planning. In this paper, we propose a hybrid approach to constructing indoor radio maps by developing a novel indoor signal propagation model, called the Hidden Environment Model (HEM). The model is a hybrid because it combines on-site measurements with a number of different types of calculations. As part of this model, we introduce the idea of an Environment Factor Matrix (EFM). The EFM represents a model of the environmental features that affect radio attenuation. We also develop a Lazy Sampling Algorithm to help generate the EFM. The goal of the Lazy Sampling Algorithm is to balance the number of measurements that need to be taken with our model's accuracy. The goal is to minimize the measurement workload while maintaining satisfactory accuracy. We evaluate our model by comparing the radio maps calculated from the model to a radio map obtained by exhaustive measurements. The results show that our Hidden Environment Model achieves good accuracy.

Geostatistical Techniques for Practical Wireless Network Coverage Mapping

The problem of mapping the extent of “usable” coverage of an existing wireless network is important in a large number of applications, including communicating the abilities of the network to users, identifying coverage gaps and planning expansion, discovering opportunities for spectrum reuse, and determining possible sources of interference with other networks. This thesis addresses fundamental but unsolved problems of measurement-based wireless coverage mapping: where should measurements be made, how many are necessary, and what can be said about the coverage at points that have not been measured. To address these problems, this thesis advocates a geostatistical approach using optimized spatial sampling and ordinary Kriging. A complete system for coverage mapping is developed that systematically addresses measurement, sampling, spatial modeling, interpolation, and visualization. This geostatistical method is able to produce more accurate and robust coverage maps than the current state of the art methods, and is able to discover coverage holes as effectively as dedicated heuristic methods using a small number of measurements. Several important practical extensions are investigated: applying these methods to drive-test measurements which have been resampled to alleviate effects from sampling bias, and crowd-sourced coverage mapping applications where volunteer-collected measurements may be sparse or infrequent. The resulting maps can then be refined iteratively, and updated systematically over time using an optimized iterative sampling scheme. An extensive validation is performed using measurements of production WiFi, WiMax, GSM, and LTE networks in representative urban and suburban outdoor environments