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).

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems “organically, ” however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

Abstract — We investigate the impact of landmark placement on localization performance using a combination of analytic and experimental analysis. For our analysis, we have derived an upper bound for the localization error of the linear least squares algorithm. This bound reflects the placement of landmarks as well as measurement errors at the landmarks. We next develop a novel algorithm, maxL−minE, that using our analysis, finds a pattern for landmark placement that minimizes the maximum localization error. To show our results are applicable to a variety of localization algorithms, we then conducted a series of localization experiments using both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in a real building environment. We use both Received Signal Strength (RSS) and Time-of-Arrival (ToA) as ranging modalities. Our experimental results show that our landmark placement algorithm is generic because the resulting placements improve localization performance across a diverse set of algorithms, networks, and ranging modalities. I.

Abstract. The wide availability of radio signal strength attenuation information on wireless radios has received considerable attention as a convenient means of deriving positioning information. Although some schemes have been shown to work in some scenarios, many agree that the robustness of such schemes can be easily compromised when low power IEEE 802.15.4 radios are used. Leveraging a recently installed sensor network testbed, we provide a detailed characterization of signal strength properties and link asymmetries for the CC2420 IEEE 802.15.4 compliant radio using a monopole antenna. To quantify the several factors of signal unpredictability due to the hardware, we have collected several thousands of measurements to study the antenna orientation and calibration effects. Our results show that the often overlooked antenna orientation effects are the dominant factor of the signal strength sensitivity, especially in the case of 3-D network deployments. This suggests that the antenna effects need to be carefully considered in signal strength schemes. 1

Abstract. In this paper, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We propose several performance metrics that quantify the estimator’s precision and error, including Hölder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of several point-based and areabased algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. We observed both strong experimental and theoretic evidence that all the algorithms have similar average responses to signal strength attacks. 1

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption.

Recent advances in wireless communication, low power sensors and microcontrollers enable the deployment of large-scale wireless sensor networks. Localization is a fundamental service required by many wireless sensor network applications. We consider a distributed, probabilistic approach, suitable for outdoor systems with inaccurate range measurements. The approach restricts the possible locations of the nodes by using a combination of positive and negative constraints. We reduce the computational complexity of the algorithm by using two-dimensional fast Fourier transforms (FFTs). We evaluated the proposed probabilistic approach through simulations based on real-world measurements; the results are compared with two other localization schemes and the Cramer-Rao lower bound (CRLB). The results show that, for inaccurate range measurements, the proposed probabilistic approach outperforms existing methods and approaches the CRLB. I.

The wireless received signal strength (RSS) based localization techniques have attracted significant research interest for their simplicity. The RSS based localization techniques can be divided into two categories: the distance estimation based and the RSS profiling based techniques. The path loss exponent (PLE) is a key parameter in the distance estimation based localization algorithms, where distance is estimated from the RSS. The PLE measures the rate at which the RSS decreases with distance, and its value depends on the specific propagation environment. Existing techniques on PLE estimation rely on both RSS measurements and distance measurements in the same environment to calibrate the PLE. However distance measurements can be difficult and expensive to obtain in some environments. In this paper we propose several techniques for online calibration of the PLE in wireless sensor networks without relying on distance measurements. We demonstrate that it is possible to estimate the PLE using only power measurements and the geometric constraints associated with planarity in a wireless sensor network. This may have a significant impact on distance-based wireless sensor network localization. I.

Abstract — Accurately positioning nodes in wireless and sensor networks is important because the location of sensors is a critical input to many higher-level networking tasks. However, the localization infrastructure can be subjected to non-cryptographic attacks, such as signal attenuation and amplification, that cannot be addressed by traditional security services. We propose several attack detection schemes for wireless localization systems. We first formulate a theoretical foundation for the attack detection problem using statistical significance testing. Next, we define test metrics for two broad localization approaches: multilateration and signal strength. We then derived both mathematical models and analytic solutions for attack detection for any system that utilizes those approaches. We also studied additional test statistics that are specific to a diverse set of algorithms. Our tracedriven experimental results provide strong evidence of the effectiveness of our attack detection schemes with high detection rates and low false positive rates across both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two real office buildings. Surprisingly, we found that of the several methods we describe, all provide qualitatively similar detection rates which indicate that the different localization systems all contain similar attack detection capability. I.

Abstract. Overthepastdecadewehavewitnessedtheevolutionof wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation. 1