An improved approach for RSSI-based only calibration-free real-time indoor localization on IEEE 802.11 and 802.15.4 wireless networks

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error

COMPASS: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses

Positioning systems are one of the key elements required by context-aware application and location-based services. This paper presents the design, implementation and anaylsis of a positioning system called COMPASS which is based on 802.11 compliant network infrastructure and digital compasses. On the mobile device, COMPASS samples the signal strength values of different access points in communication range and utilizes the orientation of the user to preselect a subset of the training data. The remaining training data is used by a probabilistic position determination algorithm to determine the position of the user. While prior systems show only limited accuracy due to blocking effects caused by human bodies, we apply digital compasses to detect the orientations of the users so that we can handle these blocking effects. After a short period of training our approach achieves an average error distance of less than 1.65~meters in our experimental environment of 312 square meters

An unsupervised learning technique to optimize radio maps for indoor localization

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m(2), resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data

A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation

Practical privacy enhancing technologies for mobile systems

Mobile computers and handheld devices can be used today to connect to services available on the Internet. One of the predominant technologies in this respect for wireless Internet connection is the IEEE 802.11 family of WLAN standards. In many countries, WLAN access can be considered ubiquitous; there is a hotspot available almost anywhere. Unfortunately, the convenience provided by wireless Internet access has many privacy tradeoffs that are not obvious to mobile computer users. In this thesis, we investigate the lack of privacy of mobile computer users, and propose practical enhancements to increase the privacy of these users. We show how explicit information related to the users' identity leaks on all layers of the protocol stack. Even before an IP address is configured, the mobile computer may have already leaked their affiliation and other details to the local network as the WLAN interface openly broadcasts the networks that the user has visited. Free services that require authentication or provide personalization, such as online social networks, instant messengers, or web stores, all leak the user's identity. All this information, and much more, is available to a local passive observer using a mobile computer. In addition to a systematic analysis of privacy leaks, we have proposed four complementary privacy protection mechanisms. The main design guidelines for the mechanisms have been deployability and the introduction of minimal changes to user experience. More specifically, we mitigate privacy problems introduced by the standard WLAN access point discovery by designing a privacy-preserving access-point discovery protocol, show how a mobility management protocol can be used to protect privacy, and how leaks on all layers of the stack can be reduced by network location awareness and protocol stack virtualization. These practical technologies can be used in designing a privacy-preserving mobile system or can be retrofitted to current systems