Enhanced indoor locationing in a congested Wi-Fi environment

… entity localization and tracking in GPS …, 2009

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.

Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 2019

Global positioning system and other outdoor positioning mechanisms are already subject to comprehensive research and development for almost half a century. Conversely, indoor positioning services became a hot topic in the last decade. Since GPS (and. other outdoor solutions) do not work reliably in most indoor environments, researchers and developers are working on accurate positioning solutions, especially tailored for indoor places. However; due to walls, furniture, people and other obstacles, absolute location estimation is very hard and expensive to achieve in indoor places. In addition, accuracy needs depend on the scenario and application. In this study, we have studied the feasibility of room-level location detection in home and office environments. We have focused on examining the quality of room-wise detection accuracy of the fingerprinting method that is applied along with standard Wi-Fi radio infrastructure. We have conducted experiments in a multi-storey office building made of concrete and aerated concrete bricks with many rooms, in which it is significantly hard to accurately estimate the correct place of a thing, using radio signals. To the best of our knowledge, our paper is the first study that investigates the room-level accuracy of Wi-Fi fingerprinting-based indoor localization systems. We have found out that, it is possible to feasibly achieve room-level detection with good accuracy, via a pre-calculated room-specific received signal strength indicator threshold value.

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.

2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, 2007

Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of fingerprint-based location systems in order to enhance their accuracy. First, we derive analytically a robust location fingerprint definition, and then verify it experimentally as well. We also devise a way to facilitate under-trained location systems through simple linear regression technique. This technique reduces the training time and effort, and can be particularly useful when the surrounding or setup of the localization area changes. We further show experimentally that because of the positions of some access points or the environmental factors around them, their signal strength correlates nicely with distance. We argue that it would be more beneficial to give special consideration to these access points for location computation, owing to their ability to distinguish locations distinctly in signal space. The probability of encountering such access points will be even higher when we denote a location's signature using the signals of multiple wireless technologies collectively. We present the results of two well-known localization algorithms (K-Nearest Neighbor and Bayesian Probabilistic Model) when the above factors are exploited, using Bluetooth and Wi-Fi signals. We have observed significant improvement in their accuracy when our ideas are implemented.

2013

Whilst access to location based information has been mostly possible in the outdoor arena through the use of GPS, the provision of accurate positioning estimations and broad coverage in the indoor environment has proven somewhat problematic to deliver. Considering more time is spent in the indoor environment, the requirement for a solution is obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings and floors etc. muffling the signals to\from mobile devices and their tracking devices, is one of the many barriers to implementation. Moreover the characteristically noisy behaviour of wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause interference as they all operate in the same band as Wi-Fi devices. The limited range of tracking devices such as Wireless Access Points (AP), and the restrictions surrounding their positioning within a buildings’ infrastructure further exacerbate this issue, these difficulties provide a...

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2013

This paper describes our experiences and observations with a localization system that continuously tracks the indoor location of a large number of consumer mobile devices. Unlike past work that focuses principally on the accuracy of the location tracking algorithm, we study the performance of the localization system in terms of key additional metrics: scalability and energy-efficiency, which can sometimes conflict with the desire for high accuracy. To ensure that our solution can handle both Android and iOS-based mobile devices (& other closed mobile platforms), we adapt the conventional clientside fingerprinting-based localization approaches to develop a novel and practical infrastructure-based location tracking strategy. We study the relative accuracy to the two approaches in two different types of indoor buildings. Our studies establish how the building and its occupancy characteristics affect the accuracy achievable by different algorithms, and provide insights into why scalable, energy efficient and accurate indoor location tracking remains a challenge in practice.

2011

IEEE Transactions on Mobile Computing, 2000

Recently, the positioning techniques based on the IEEE 802.11 signal strength are becoming the dominant solutions in the mobile device localization within indoor scenarios. Such solutions are characterized by two main pitfalls that compromise their effective usage in real application environments. First, during the calibration, a large amount of manual effort is required for acquiring a massive collection of training samples. Second, the positioning accuracy is directly related to the deployment of the wireless access points into the workspace, which is extremely time-consuming and requires human intervention. This paper presents an approach to reduce the manual calibration and to optimize the positioning accuracy, by selecting the best deployment schema of the wireless access points. The approach has been implemented in a tool, which uses an analytical signal propagation model to build the radio map of a given workspace, and exploits a multi-objective genetic algorithm to identify the best access points placement pattern that fits the required accuracy. A detailed experimental campaign is presented in order to show the benefits achievable by the proposed approach.