Avoiding multipath to revive inbuilding WiFi localization

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.

International Conference on Indoor Positioning and Indoor Navigation, 2013

This paper proposes a novel approach to the problem of indoor location estimation based on IEEE 802.11 (WiFi) signals. The approach uses a rotating directional antenna at the Access Point (AP). The Mobile Device (MD) measures the variation of Received Signal Strength Indication (RSSI) with antenna Direction Of Emission (DOE). The observed DOE-RSSI signature is used as a fingerprint to search a map of previously measured DOE-RSSI signatures. The method has the advantage that it works with existing 802.11 cards and that it provides higher accuracy, for the same number of APs, than the conventional RSSI fingerprinting technique based on maps obtained using multiple omnidirectional APs. We report results obtained from an experimental testbed deployed in a typical office space. The Lighthouse algorithm achieved 85% and 95% success ratio in MD localisation over 20 possible positions using one AP and two APs, respectively. The success ratio achieved is, on average, +24% and +53% better than the conventional RSSI fingerprinting and directional beaconing methods, respectively.

ACM SIGSPATIAL, 2017

WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually by site surveyors. This process is labour intensive, tedious, and needs to be repeated with any change in the environment. While a number of recent systems have been introduced to reduce the calibration effort through RF propagation models and/or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones. In this paper, we present HybridLoc: an accurate low-overhead indoor localization system. The basic idea HybridLoc builds on is to leverage the sensors of high-end phones to enable localization of lower-end phones. Specifically, the WiFi fingerprint is crowd-sourced by opportunistically collecting WiFi-scans labeled with location data obtained from BLE-enabled high-end smart phones. These scans are used to automatically construct the WiFi-fingerprint, that is used later to localize any lower-end cell phone with the ubiquitous WiFi technology. HybridLoc also has provisions for handling the inherent error in the estimated BLE locations used in constructing the fingerprint as well as to handle practical deployment issues including the noisy wireless environment, heterogeneous devices, among others. Evaluation of HybridLoc using Android phones shows that it can provide accurate localization in the same range as manual fingerprinting techniques under the same deployment conditions. Moreover, the localization accuracy on low-end phones supporting only WiFi is comparable to that achieved with high-end phones supporting BLE. This accuracy is achieved with no training overhead , is robust to the different user devices, and is consistent under environment changes.

Proceedings of the 18th annual international conference on Mobile computing and networking, 2012

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 ∼ 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFibased methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013

Device-free (DF) indoor localization has grasped great attention recently as a value-added service to the already installed WiFi infrastructure as it allows the tracking of entities that do not carry any devices nor participate actively in the localization process. Current approaches, however, require a relatively large number of wireless streams, i.e. transmitterreceiver pairs, which is not available in many typical scenarios, such as home monitoring.

IEEE Transactions on Mobile Computing

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed.

… entity localization and tracking in GPS …, 2009

IEEE Transactions on Mobile Computing, 2000

Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both access-point-based localization and mobile-node-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD based algorithms have better accuracy.

This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.

The past few years have seen wide spread adoption of outdoor positioning services, mainly GPS, being incorporated into everyd ay devices such as smartphones and tablets. While outdoor positioning has been well received by the public, its indoor counterpart has been mostly limited to private use due to its higher costs and complexity for setting up the proper environment. The objective of this research is to provide an affordable mean for indoor localization using wireless local area network (WLAN) Wi-Fi technology. We combined two different Wi-Fi approaches to locate a user. The first method involves the use of matching the pre-recorded received signal strength (RSS) from nearby access points (AP), to the data transmitted from the user on the fly. This is commonly known as "fingerprint matching". The second approach is a distance-based trilateration approach using three known AP coordinates detected on the user"s device to derive the position. The combination of the two steps enhances the accuracy of the user position in an indoor environment allowing location-based services (LBS) such as mobile augmented reality (M AR) to be deployed more effectively in the indoor environment. The mapping of the RSS map can also prove useful to IT planning personnel for covering locations with no Wi-Fi coverage (ie. dead spots). The experiments presented in this research helps provide a foundation for the integration of indoor with outdoor positioning to create a seamless transition experience for users.

2015 IEEE International Conference on Communications (ICC), 2015

The pervasive diffusion of smartphones is boosting indoor positioning solutions and location-based services. We propose a novel methodology to perform indoor positioning of mobile users by the estimation of angles of arrival from access points whose locations are known. Angles of arrival are estimated by correlating WiFi RSSI measurements with data coming from a digital compass, which is provided by most current handsets. Our system has minimal requirements in terms of infrastructure and mobile hardware. The system neither needs calibration, nor radio maps but requires the user to perform a gesture when an estimation is needed. The resulting on-demand localization has advantages in terms of privacy and power efficiency. Initial experimental results, even under severe multipath conditions, show good accuracy in terms of angle of arrival estimation and promising results on localization.

2018 IEEE Global Communications Conference (GLOBECOM), 2018

This paper proposes an economical, nonintrusive, and high-precision indoor localization scheme based on Wi-Fi fingerprinting that requires only a single Wi-Fi access point and a single fixed-location receiver. A deep neural network (DNN) based classification model is trained with Wi-Fi channel state information (CSI) fingerprints for localizing the target without any device attached (i.e., device-free). CSI provides finergrained information than received signal strength (RSS). CSI pre-processing based on singular value decomposition (SVD), as well as data augmentation based on noise injection and interperson interpolation, are incorporated into the proposed DNN framework for enhanced robustness and performance. Realworld experiments examine two scenarios with different degrees of target similarity and show that the proposed DNN-based system can consistently improve the localization performance as compared to the original DNN model.

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.

2009

Context-aware applications for indoor intelligent environments require an appropriately accurate and stable interior positioning system to adapt services to the location of a mobile user or mobile device in a building. Different technologies provide a varying mix of resolution, accuracy, stability and challenges. In this paper we report on our experience using an existing Wi-Fi infrastructure without specialized hardware added to support location tracking. There are several approaches to track the location of Wi-Fi enabled devices within a building such as signal propagation models and signature matching. We found signature matching most effective in our environment. Signature matching is accomplished by storing Wi-Fi signatures (signal strengths observed for several detectable access points) for each room and comparing the current signature on the device to stored signatures to find the closest match. In this paper we explain experiments we conducted to explore and optimize Wi-Fi location tracking in one building. While we had hoped for more accurate positioning, we found that only room-level granularity was consistently and reliably achieved. The accuracy of Wi-Fi location tracking is improved as more signature points are stored, but is significantly reduced by the presence of people moving in the area. It also appears that strategically placed access points within a building can contribute to optimum room-level disambiguation of location. Use of a histogram of signal strengths for signatures at a single location may offer a good compromise between a single average and storing a large number of signatures needed for improved accuracy.

2007 IEEE International 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.