WLAN Location Sharing through a Privacy Observant Architecture

In the last few years, WLAN has seen immense growth and it will continue this trend due to the fact that it provides convenient connectivity as well as high speed links. Furthermore, the infrastructure already exists in most public places and is cheap to extend. These advantages, together with the fact that WLAN covers a large area and is not restricted to line of sight, have led to developing many WLAN localization techniques and applications based on them. In this paper we present a novel calibration-free localization technique using the existing WLAN infrastructure that enables conference participants to determine their location without the need of a centralized system. The evaluation results illustrate the superiority of our technique compared to existing methods. In addition, we present a privacy observant architecture to share location information. We handle both the location of people and the resources in the infrastructure as services, which can be easily discovered and used. An important design issue for us was to avoid tracking people and giving the users control over who they share their location information with and under which conditions

Performance Evaluation of Mobile U-Navigation based on GPS/WLAN Hybridization

This paper present our mobile u-navigation system. This approach utilizes hybridization of wireless local area network and Global Positioning System internal sensor which to receive signal strength from access point and the same time retrieve Global Navigation System Satellite signal. This positioning information will be switched based on type of environment in order to ensure the ubiquity of positioning system. Finally we present our results to illustrate the performance of the localization system for an indoor/ outdoor environment set-up.Comment: Journal of Convergence Information Technology(JCIT

Location-aware computing: a neural network model for determining location in wireless LANs

The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Multiverse: Mobility pattern understanding improves localization accuracy

Department of Computer Science and EngineeringThis paper presents the design and implementation of Multiverse, a practical indoor localization system that can be deployed on top of already existing WiFi infrastructure. Although the existing WiFi-based positioning techniques achieve acceptable accuracy levels, we find that existing solutions are not practical for use in buildings due to a requirement of installing sophisticated access point (AP) hardware or special application on client devices to aid the system with extra information. Multiverse achieves sub-room precision estimates, while utilizing only received signal strength indication (RSSI) readings available to most of today's buildings through their installed APs, along with the assumption that most users would walk at the normal speed. This level of simplicity would promote ubiquity of indoor localization in the era of smartphones.ope

Transparent Location Fingerprinting for Wireless Services

Detecting the user location is crucial in a wireless environment, not only for the choice of first-hop communication partners, but also for many auxiliary purposes: Quality of Service (availability of information in the right place for reduced congestion/delay, establishment of the optimal path), energy consumption, automated insertion of location-dependent info into a web query issued by a user (for example a tourist asking informations about a monument or a restaurant, a fireman approaching a disaster area). The technique we propose in our investigation tries to meet two main goals: transparency to the network and independence from the environment. A user entering an environment (for instance a wireless-networked building) shall be able to use his own portable equipment to build a personal map of the environment without the system even noticing it. Preliminary tests allow us to detect position on a map with an average uncertainty of two meters when using information gathered from three IEEE802.11 access points in an indoor environment composed of many rooms on a 625sqm area. Performance is expected to improve when more access points will be exploited in the test area. Implementation of the same techniques on Bluetooth are also being studied