Indoor Localization Using Uncooperative Wi-Fi Access Points

2010

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, we ask the question: can we perform indoor localization with no pre-deployment effort? Our setting is an indoor space, such as an office building or a mall, with WiFi coverage but where we do not assume knowledge of the physical layout, including the placement of the APs. Users carrying WiFi-enabled devices such as smartphones traverse this space in normal course. The mobile devices record Received Signal Strength (RSS) measurements corresponding to APs in their view at various (unknown) locations and report these to a localization server. Occasionally, a mobile device will also obtain and report a location fix, say by obtaining a GPS lock at the entrance or near a window. The centerpiece of our work is the EZ Localization algorithm, which runs on the localization server. The key intuition is that all of the observations reported to the se...

TENCON 2010 - 2010 IEEE Region 10 Conference, 2010

The best candidate to determine a user location in indoor environment is by using IEEE 802.11 (Wi-Fi) signals, since it is more and more widely available and installed on most mobile devices used by users. Unfortunately, the signal strength, signals quality and noise of Wi-Fi, in worst scenario, fluctuate up to 33% because of the reflection, refraction, temperature, humidity, the dynamic environment, etc. This makes problems in determining a user location indoor. This study presents our current development on a light-weight algorithm, which is designed to be easy, simple but robust in producing the determination of user location. We study user location accuracy and the error levels to verify the mislead determination of the user location and improve the accuracy of the user location based on "multiple observers" ηk-Nearest Neighbor.

Despite of several years of innovative research, indoor localization is still not mainstream. Existing techniques either employ cumbersome fingerprinting, or rely upon the deployment of additional infrastructure. Towards a solution that is easier to adopt, we propose CU P I D, which is free from these restrictions, yet is comparable in accuracy. While existing WiFi based solutions are highly susceptible to indoor multipath, CUPID utilizes physical layer (PHY) information to extract the signal strength and the angle of only the direct path, successfully avoiding the effect of multipath reflections. Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP). Real-world indoor experiments using off-the-shelf wireless chipsets confirm the feasibility of CUPID. In addition, while previous approaches rely on multiple APs, CUPID is able to localize a device when only a single AP is present. When a few more APs are available, CUPID can improve the median localization error to 2.7m, which is comparable to schemes that rely on expensive fingerprinting or additional infrastructure.

2017

Alternative indoor localization methods have long been researched due to the unsuitability of GPS and cellular technology in indoor environments. Indoor localization systems find their applications in many areas such as targeted advertising, inventory management and tracking, navigation in museums, arenas, hospitals and in first responder and disaster situations like fire. Wireless localization systems have become very popular in recent years, especially the ones based on Wi-Fi due to its existence in almost any indoor environment today. Our indoor localization system is based on Wi-Fi (IEEE 802.11g) and can be installed on existing wireless devices with a simple software patch. This is very attractive since there are a hundreds of millions of wireless devices already in use and a hardware upgrade would be impractical. Our indoor localization system consists of a target node unaware of its location and several reference peer nodes which are location aware. Target nodes communicate o...

2014

Localization for indoor environment normally does not use GPS signals since it cannot penetrate through walls and buildings. Instead, many works have focused on using Wi-Fi signals as the mean to locate the position of the mobile devices. However, most of these approaches require a training step to build a Wi-Fi’s map for each location. This requirement practically prevents these approaches from being realistic, since the training step is extremely time-consuming (hundreds of labor hours). Recently, ISIL has been proposed as the first Wi-Fi-based technique that is training-free, in which the localization can be done instantly at any location without the need of training and building Wi-Fi map. ISIL collects from the web the related information of all observable access points and infers the current position based on that. As the first search-based Wi-Fi localization, ISIL removes the unacceptable time-consuming training step. However, it still does not provide adequate accuracy due t...

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.

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.

International Journal of Ambient Computing and Intelligence, 2014

WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points (AP) mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes o...

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.

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