On the Placement of Wi-Fi Access Points for Indoor Localization

2009

Many context-aware mobile applications require a reasonably accurate and stable estimate of a user's location. While the Global Positioning System (GPS) works quite well worldwide outside of buildings and urban canyons, locating an indoor user in a real-world environment is much more problematic. Several different approaches and technologies have been explored, some involving specialized sensors and appliances, and others using increasingly ubiquitous Wi-Fi and Bluetooth radios. In this project, we want to leverage existing Wi-Fi access points (AP) and seek efficient approaches to gain usefully high room-level accuracy of the indoor location prediction of a mobile user. The Redpin algorithm, in particular, matches the Wi-Fi signal received with the signals in the training data and uses the position of the closest training data as the user's current location. However, in a congested Wi-Fi environment where many APs exist, the standard Redpin algorithm can become confused because of the unstable radio signals received from too many APs. In this paper, we propose several enhanced indoor-locationing algorithms for the congested Wi-Fi environment. Different statistical learning algorithms are compared and empirical results show that: using more neighbors gives better results than using the 1-best neighbor; weighting APs with the correlation between the AP visibility and the location is better than the equally weighted AP combination, and automatic filtering noisy APs increases the overall detection accuracy. Our experiments in a university building show that our enhanced indoor locationing algorithms significantly outperform the-state-of-the-art Redpin algorithm. In addition, this paper also reports our findings on how the size of the training data, the physical size of the room and the number of APs affect the accuracy of indoor locationing.

Wireless Access Point Positioning Optimization, 2019

This work presents the development of a wireless Access Point (AP) placement software that uses microwave signal propagation models. We use the Simulated Annealing metaheuristic, to improve the signal coverage according to the physical features of the architectural floor plan. In the experiments, a Graphics Processing Unit (GPU) with CUDA is used to parallelize the simulations, speeding up the runtime required . It is worth noting that our software makes possible to test the provisions of APs without the operational cost of having to move them physically. It proposes a spatial arrangement of APs that provides a greater coverage with usable signal intensity within the simulated environment. Using a limited amount of APs, the test case results showed that it is possible to obtain a significant improvement in the coverage of the Wi-Fi signal by adjusting the APs positions to the metaheuristic proposed solution. In addition, our software is capable of performing simulations with an increasing number of APs if the current quantity is not enough to achieve better quality and signal coverage.

The past decade has witnessed substantial research on methods for indoor Wi-Fi positioning. While much effort has gone into achieving high positioning accuracy and easing fingerprint collection, it is our contention that the general problem is not sufficiently well understood, thus preventing deployments and their usage by applications to become more widespread. Based on our own and published experiences on indoor Wi-Fi positioning deployments, we hypothesize the following: Current indoor Wi-Fi positioning systems and their utilization in applications are hampered by the lack of understanding of the requirements present in the real-world deployments. In this paper, we report findings from qualitatively studying organisational requirements for indoor Wi-Fi positioning. The studied cases and deployments cover both company and public-sector settings and the deployment and evaluation of several types of indoor Wi-Fi positioning systems over durations of up to several years. The findings suggest among others a need for supporting all case-specific user groups, providing software platform independence, low maintenance, allowing positioning of all user devices, regardless of platform and form factor. Furthermore, the findings also vary significantly across organisations, for instance in terms of need for coverage, which motivates the design of orthogonal solutions.

In mobile computing scenarios, context-aware applications are more effective in relieving from the mobile user the burden of introducing information that can be automatically derived from the environment. In particular, the physical position of the mobile system (and hence of the user) is fundamental for many types of applications. User position estimation methods based on strength of the radio signals received from multiple wireless access points have been recently proposed and implemented by several independent research groups. In this paper a new approach to wireless access point placement is proposed. While previous proposals focus on optimal coverage aimed at connectivity, the proposed method integrates coverage requirements with the reduction of the error of the user position estimate. In particular, this paper proposes a mathematical model of user localization error based on the variability of signal strength measurements. This model has been designed to be independent from the actual localization technique, therefore it is only based on generic assumptions on the behavior of the localization algorithm employed. The proposed error model is used by local search heuristic techniques, such as local search, a prohibition-based variation and simulated annealing. Near-optimal access point placements are computed for various kinds of optimization criteria: localization error minimization, signal coverage maximization, a mixture of the two. The different criteria are not expected to be compatible: maximizing signal coverage alone can lead to degradation of the average positioning error, and vice versa. Some experiments have been dedicated to quantify this phenomenon and to introduce possible trade-offs.

2015

A Master of Science thesis in Electrical Engineering by Ayah Mahmoud Abusara entitled, "Indoor Positioning Techniques and Approaches for WI-FI Based Systems," submitted in June 2015. Thesis advisor is Dr. Mohamed Hassan. Soft and hard copy available.

Sensors, 2015

With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points' self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.

2014 10th International Conference on Innovations in Information Technology (IIT), 2014

The wide deployment of Wi-Fi networks empowers the implementation of numerous applications such as Wi-Fi positioning, Location Based Services (LBS), wireless intrusion detection and real-time tracking. Many techniques are used to estimate Wi-Fi client position. Some of them are based on the Time or Angle of Arrival (ToA or AoA), while others use signal power measurements and fingerprinting. All these techniques require the reception of multiple wireless signals to provide enough data for solving the localization problem. In this paper, we describe the major techniques used for positioning in Wi-Fi networks. Real experiments are done to compare the accuracy of methods that use signal power measurement and Received Signal Strength Indication (RSSI) fingerprinting to estimate client position. Moreover, we investigate a fingerprinting method constrained by distance information to improve positioning accuracy. Localization techniques are more accurate when the estimated client positions are closer to the real geographical positions. Accuracy improvements increase user satisfaction, and make the localization services more robust and efficient.

Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To ad-dress this problem, a new method for improving the indoor posi-tioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting meth-od to identify the initial position estimate of the smartphone us-er. Then two distance correction values in two roughly perpen-dicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

Acta Polytechnica Hungarica, 2015

Location-based applications and services are becoming widespread with the proliferation of smart mobile devices. They require position tracking which is still a challenge today in the indoor environment. Different wireless technologies and localization techniques can be used to obtain the position estimation. One possibility is deploying cheap ZigBee sensors as reference points and using triangulation to compute the position of the visiting node. This technique requires the reception of a wireless signal of at least three reference sensors with well-known positions everywhere within the covered area. In this paper, we propose OptiRef, our simulated annealing based method to find, in a given area, the optimal number and placement of the reference sensors to be used for indoor positioning. Our method has O(n) complexity and shows linear run-time behavior. We investigate the performance of OptiRef via simulations focusing on ZigBee technology, although our method is generic and can be used with any kind of wireless technologies. The resulted reference point setup(s) can be considered as a good starting point for real environment design.