Received Signal Strength Fingerprints Research Articles

Sparsity-aware multi-source RSS localization

We tackle the problem of localizing multiple sources in multipath environments using received signal strength (RSS) measurements. The existing sparsity-aware fingerprinting approaches only use the RSS measurements (autocorrelations) at different access points (APs) separately and ignore the potential information present in the cross-correlations of the received signals. We propose to reformulate this problem to exploit this information by introducing a novel fingerprinting paradigm which leads to a significant gain in terms of number of identifiable sources. Besides, we further enhance this newly proposed approach by incorporating the information present in the other time lags of the autocorrelation and cross-correlation functions. An interesting by-product of the proposed approaches is that under some conditions we can convert the given underdetermined problem to an overdetermined one and efficiently solve it using classical least squares (LS). Moreover, we also approach the problem from a frequency-domain perspective and propose a method which is blind to the statistics of the source signals. Finally, we incorporate the so-called concept of finite-alphabet sparsity in our framework for the case where the sources have a similar power. Our extensive simulation results illustrate a good performance as well as a significant detection gain for the introduced multi-source RSS fingerprinting methods.

Multiple targets localization approach based on virtual fingerprinting in indoor environment

The main drawbacks of indoor localization approach based on Received Signal Strength (RSS) is the sensitivity to the fluctuation of measured RSS at a fixed location in difference time. In this paper, a novel multiple target localization approach is proposed by exploiting virtual fingerprinting, which is calculated from spacial relationship between reference nodes and virtual location nodes. The merits of our proposed method is twofold. First, our proposed method does not require the process of RSS fingerprinting constructing, which is a huge burden of localization methods based on RSS. Secondly, our proposed method is more robust than some existing methods, such as TOA-based method and RSS-based method, in big measurement error case. The efficacy of our proposed method are proven by means of some experimental results.

RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

An important requirement for many novel location based services, is to determine the locations of people, equipment, animals, etc. The accuracy and response time of estimation are critical issues in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed semi-supervised location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between Received Signal Strength fingerprint and their respective location in a subsystem. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. On each subsystem at first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user's location. It is shown that the proposed Distributed Semi-Supervised Locally Linear Embedding scheme has the advantage of robustness, scalability, useful in large site application and is easy in training and implementation. We have compared our results with Distributed Subtract on Negative Add on Positive (DSNAP) and benchmark method RADAR. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site as well as with DSNAP and benchmark method RADAR.

Fault detection and mitigation in WLAN RSS fingerprint-based positioning

Fingerprint-based positioning methods that utilise WLAN received signal strength (RSS) measurements are becoming increasingly popular for inferring terminal location in indoor environments. They are very attractive mainly because of the low deployment cost, as they exploit the ubiquity of WLAN access points (AP) and the worldwide availability of mobile devices featuring WLAN adapters for wireless connectivity. They provide reliable and accurate location estimates, however their performance can be compromised when faults occur in the overall positioning system. Such faults are injected in different ways, including the removal or relocation of one or several APs, and are caused either accidentally or deliberately due to malicious attacks. These faults corrupt the RSS fingerprints and may lead to accuracy degradation, unless countermeasures are taken. In this work, we focus on positioning under faults and construct detection schemes using fault indicators that are applicable to standard fingerprint-based methods and achieve high fault detection rates. In addition, we incorporate our detection mechanisms into hybrid positioning algorithms that greatly improve fault tolerance in the presence of different types of faults. Finally, we present experimental results to verify the effectiveness of the proposed approach.

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments.

Multi-Band Received Signal Strength Fingerprinting Based Indoor Location System

Multi-Band Received Signal Strength Fingerprinting Based Indoor Location System

Securing Wi-Fi networks with position verification: extended version

In this paper we investigate position-based enhancements to Wi-Fi network security. Specifically, we investigate whether Received Signal Strength (RSS) measurements can identify attempts at network access by malicious nodes exterior to an authorised network perimeter. We assume the malicious nodes will spoof their received or transmitted power-levels in attempts to circumvent standard position-based security techniques. We outline why residual analysis of the RSS measurements cannot robustly identify illegal network access requests. However, we show that by referring the residual RSS analysis to a 'claimed position', interior to the authorised perimeter, a robust position-based verification system for secure network access can be developed. Indoor systems based on RSS fingerprints and differential RSS fingerprints are studied. Outdoor systems under the assumption of log-normal shadowing are also investigated.