Cramér-Rao bound analysis of localization using signal strength difference as location fingerprint

In this paper, we analyze the Cramér-Rao Lower Bound (CRLB) of localization using Signal Strength Difference (SSD) as location fingerprint. This analysis has a dual purpose. Firstly, the properties of the bound on localization error may help to design efficient localization algorithm. For example, utilizing one of the properties, we propose a way to define weights for a weighted K-Nearest Neighbor (K-NN) scheme which is shown to perform better than the K-NN algorithm. Secondly, it provides suggestions for a positioning system design by revealing error trends associated with the system deployment. In both cases, detailed analysis as well as experimental results are presented in order to support our claims

A comprehensive study of Bluetooth signal parameters for localization

We provide an elaborate discussion on Bluetooth signal parameters with respect to localization, whereby we collectively designate all types of Bluetooth specification parameters that are related to signal strength - such as RSSI, Link Quality, Received and Transmit Power Level - as Bluetooth signal, parameters. According to our analysis and experimental results, "RSSI" and "Transmit Power Level" turn out to be poor candidates for localization, while "Link Quality" has its limitations. On the other hand, "Received Power Level" correlates nicely with distance, which makes it the most desirable Bluetooth signal parameter to be used in location systems. We contend that it is vital to choose the appropriate signal parameter in Bluetooth location systems, and we expect our work to provide useful pointers in any future design of such systems. Existing systems can also benefit by adopting the appropriate Bluetooth signal parameter in their systems, and thereby, improve their location accuracy

Fingerprint-based location estimation with virtual access points

Location fingerprinting techniques generally make use of existing wireless network infrastructure. Consequently, the positions of the access points (APs), which constitute an integral part of a location system, will invariably be dictated by the network administrator's convenience regarding data communication. But the localization accuracy of fingerprint-based solutions is largely dependent on the APs' placements over the area. In this paper, we developed the idea of virtual access point (VAP), where one can have AP's functionality at a desired position for localization purpose, without physically placing an AP there. We argue that, placing VAPs at favorable positions helps to improve localization accuracy. VAP also serves the purpose of virtually increasing the number of APs over the localization area, which according to previous works should enhance the localization accuracy further. We test the feasibility of our VAP idea both analytically and experimentally. Finally, we present our results using a well-known localization algorithm, namely, k-nearest neighbor, when our VAP idea is implemented. The findings are quite encouraging, which report significant improvement in the localization accuracy

SSD: A robust RF location fingerprint addressing mobile devices' heterogeneity

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(AP)-based localization and Mobile-Node (MN)-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