Zero-Configuration, Robust Indoor Localization: Theory and Experimentation

Abstract

Abstract-With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoretical base and develop a localization algorithm for building a zeroconfiguration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance.The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters).

A Statistical Indoor Localization Method for Supporting Location-based Access Control

“…We compare the location error of our method with that of others and show the results in Table 2.1. From the table, we can see that the estimation accuracy of our method is higher than that of RADAR [3] by 40%∼60% and Lim's approach [11] by 30%.…”

“…which means that the environmental model learned in the offline phase may not be suitable for the data collected in the online phase. Thus, they proposed a Zero-Configuration system [11] which updates the environmental model continuously without an offline phase. However, this system assumes a simple linear relationship between a signal strength value and a distance.…”

“…The challenge is how to determine locations accurately and efficiently, especially in indoor environments. Existing indoor approaches utilize different types of signals such as infrared [15], ultrasound [10], and radio frequency (RF) [3,16,11,12,17,13] to estimate locations. Among these approaches, the RF-based ones are the most promising, because they can be easily integrated with existing and widely spread 802.11 infrastructure.…”

“…Among these approaches, the RF-based ones are the most promising, because they can be easily integrated with existing and widely spread 802.11 infrastructure. RF-based approaches can be further classified depending on the metrics they measure, such as Triangulation [12], Time of Arrival (ToA) [17], Time Difference of Arrival (TDoA) [13] and Received Signal Strength (RSS) [3,16,11].…”

“…Lim et al [11] observed that the indoor environments are time-variant. Lim proposed using real-time measurements for addressing environment dynamics and hence the offline phase is unneeded in their approach.…”