Eiman Elnahrawy

The limits of localization using signal strength: a comparative study

We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10 ft and 97th percentile of 30 ft. We present strong evidence that these limitations are fundamental and that they are unlikely to transcend without a fundamentally more complex environmental models or additional localization infrastructure.

Cleaning and querying noisy sensors

Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area.

Context-Aware Sensors

Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring several physical phenomena, potentially in remote harsh environments. Spatial and temporal dependencies between the readings at these nodes highly exist in such scenarios. Statistical contextual information encodes these spatio-temporal dependencies. It enables the sensors to locally predict their current readings based on their own past readings and the current readings of their neighbors. In this paper, we introduce context-aware sensors. Specifically, we propose a technique for modeling and learning statistical contextual information in sensor networks. Our approach is based on Bayesian classifiers; we map the problem of learning and utilizing contextual information to the problem of learning the parameters of a Bayes classifier, and then making inferences, respectively. We propose a scalable and energy-efficient procedure for online learning of these parameters in-network, in a distributed fashion. We discuss applications of our approach in discovering outliers and detection of faulty sensors, approximation of missing values, and in-network sampling. We experimentally analyze our approach in two applications, tracking and monitoring.

Adding Angle of Arrival Modality to Basic RSS Location Management Techniques

In this paper, we describe a radio-based localization approach that is based on the use of rotating directional antennas and a Bayesian network that combines both angle-of-arrival (AoA) and received signal strength (RSS). After describing our network, we extensively characterize the accuracy of our approach under a variety of measured signal distortion types. Next, using a combination of synthetic and trace-driven experiments, we show the impact of different signal distortions on localization performance. We found the use of directional antennas was effective at averaging out multi-path effects in indoor environments, which helped reduce the amount of training data required compared to previous approaches.

Using area-based presentations and metrics for localization systems in wireless LANs

We show the utility of WLAN localization using areas and volumes as the fundamental localization unit. We demonstrate that area-based algorithms have a critical advantage over point-based approaches because they are better able to describe localization uncertainty, which is a common theme across WLAN based localization systems. Next, we present two novel area-based algorithms. To evaluate area-based approaches, we introduce several new localization performance metrics. We then evaluate the two algorithms using our metrics with data collected from our local WLAN. Finally, we compare our area-based algorithms against traditional point-based approaches. We find that using areas improves the ability of the localization system to give users meaningful alternatives in the face of position uncertainty.

Poster abstract: online data cleaning in wireless sensor networks

We present our ongoing work on data quality problems in sensor networks. Specifically, we deal with the problems of outliers, missing information, and noise. We propose an approach for modeling and online learning of spatio-temporal correlations in sensor net-works. We utilize the learned correlations to discover outliers and recover missing information. We also propose a Bayesian approach for reducing the effect of noise on sensor data online.

Bayesian localization in wireless networks using angle of arrival

Using existing wireless communication networks as a localization infrastructure promises enormous cost and deployment savings over specific localization infrastructures. In this work we investigate a Bayesian network approach that uses a combination of radio signal strength (RSS) to distance estimation along with angle-of-arrival (AoA) information. We characterize the resulting localization accuracy using data collected outdoors using different radios, indoor data, and simulated data. We show how the localization performance degrades in indoor environments and analyze the different sources of errors that cause this performance degradation as compared to outdoor settings. We found our network is quite sensitive to variations in the distance to signal strength, and the additional angle information had only a small impact on localization accuracy.

GRAIL: a general purpose localization system

Purpose – The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.Design/methodology/approach – In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.Findings – The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.Originality/value – A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of ...

Studying the utility of tracking systems in improving healthcare workflow

This paper describes tracking experiments we ran in medical settings in order to study the utility of RTLS systems in modeling hospitals workflows in real time. It reports some lessons we learned regarding the density of the tracking landmark deployment as well as the power and mounting of the tracking tags and their effect on the tracking accuracy. It then discusses how we deduced the clinical activities along the workflow from the RTLS tracking data.

GRAIL: general real-time adaptable indoor localization

Yingying Chen†, Eiman Elnahrawy‡, John-Austen Francisco†, Konstantinos Kleisouris†, Xiaoyan Li∗, Hongyi Xue†, Richard P. Martin†,‡ †{yingche,deymious,kkonst,xuehy58,rmartin}@cs.rutgers.edu ‡eiman@kordinate.com, ∗xili@cs.lafayette.edu †Dept. of Computer Science ‡Kordinate LLC ∗Dept. of Computer Science Rutgers University 200 Centennial Ave., Ste 200 Lafayette College Piscataway, NJ 08854 Piscataway, NJ 08854 Easton, PA 18042