Krishna Chintalapudi

A wireless sensor network For structural monitoring

Structural monitoring---the collection and analysis of structural response to ambient or forced excitation--is an important application of networked embedded sensing with significant commercial potential. The first generation of sensor networks for structural monitoring are likely to be data acquisition systems that collect data at a single node for centralized processing. In this paper, we discuss the design and evaluation of a wireless sensor network system (called Wisden for structural data acquisition. Wisden incorporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchronization. We also study the applicability of wavelet-based compression techniques to overcome the bandwidth limitations imposed by low-power wireless radios. We describe our implementation of these mechanisms on the Mica-2 motes and evaluate the performance of our implementation. We also report experiences from deploying Wisden on a large structure.

Indoor localization without the pain

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, we ask the question: can we perform indoor localization with no pre-deployment effort? Our setting is an indoor space, such as an office building or a mall, with WiFi coverage but where we do not assume knowledge of the physical layout, including the placement of the APs. Users carrying WiFi-enabled devices such as smartphones traverse this space in normal course. The mobile devices record Received Signal Strength (RSS) measurements corresponding to APs in their view at various (unknown) locations and report these to a localization server. Occasionally, a mobile device will also obtain and report a location fix, say by obtaining a GPS lock at the entrance or near a window. The centerpiece of our work is the EZ Localization algorithm, which runs on the localization server. The key intuition is that all of the observations reported to the server, even the many from unknown locations, are constrained by the physics of wireless propagation. EZ models these constraints and then uses a genetic algorithm to solve them. The results from our deployment in two different buildings are promising. Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m, respectively, in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme [29] from prior work.

Zee: zero-effort crowdsourcing for indoor localization

Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the users initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.

Monitoring civil structures with a wireless sensor network

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a buildings structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.

Fast Data Collection in Tree-Based Wireless Sensor Networks

We investigate the following fundamental question-how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

Ad-hoc localization using ranging and sectoring

Ad-hoc localization systems enable nodes in a sensor network to fix their positions in a global coordinate system using a relatively small number of anchor nodes that know their position through external means (e.g., GPS). Because location information provides context to sensed data, such systems are a critical component of many sensor networks and have therefore received a fair amount of recent attention in the sensor networks literature. The efficacy of these systems is a function of the density of deployment and of anchor nodes, as well as the error in distance estimation (ranging) between nodes. In this paper, we examine how these factors impact the performance of the system. This examination lays the groundwork for the main question we consider in this paper: Can the ability to estimate bearing to neighboring nodes greatly increase the performance of ad-hoc localization systems? We discuss the design of ad-hoc localization systems that use range together with either bearing or imprecise bearing (such as sectoring) information, and evaluate these systems using analysis and simulation.

Localized edge detection in sensor fields

A wireless sensor network that studies relatively widespread phenomena (such as a contaminant flow or a seismic disturbance) may be called upon to provide a description of the boundary of the phenomenon (either a contour or some bounding box). In such cases, it may be necessary for each node to locally determine whether it lies at (or near) the edge of the phenomenon. In this paper, we show that such localized edge detection techniques are non-trivial to design in an arbitrarily deployed sensor network. We define the notion of an edge and develop performance metrics for evaluating localized edge detection algorithms. We propose three different approaches for localized edge detection and present one example scheme for each. In all our approaches, each sensor gathers information from its local neighborhood and determines whether or not it is an edge sensor. We evaluate the performance! of each of the example schemes and compare them with respect to the developed metrics.

Structural damage detection and localization using NETSHM

Structural health monitoring (SHM) is an important application area for wireless sensor networks. SHM techniques attempt to autonomously detect and localize damage in large civil structures. Structural engineers often implement and test SHM algorithms in a higher level language such as C/Matlab. In this paper, we describe the design and evaluation of NETSHM, a sensor network system that allows structural engineers to program SHM applications in Mat-lab or C at a high level of abstraction. In particular, structural engineers do not have to understand the intricacies of wireless networking, or the details of sensor data acquisition. We have implemented a damage detection technique and a damage localization technique on a complete NETSHM prototype. Our experiments on small and medium-scale structures show that NETSHM is able to detect and localized damage perfectly with very few false-positives and no false negatives, and that it is robust even in realistic wireless environments

A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks

Communication among wireless sensor nodes that employ cheap low-power transceivers is often very sensitive to the variations of the wireless channel. Sensor network routing protocols thus strive to continually adapt to temporal variations in wireless links in order to avoid wasteful transmissions over low-quality links. Such adaptive routing protocols must rely on a scheme that can not only accurately estimate the quality of wireless links in terms of a quantitative measure, such as the packet success rate (PSR), but also quickly adapt to temporal dynamics of the links. Traditionally, the PSR is estimated from the fraction of successful transmissions over a window of test- packets. However, we demonstrate that counting based methods do not react to changes in the wireless channel fast enough and that the only way to address this problem is to estimate the PSR based on the receivers characteristics and on the signal to noise ratio (SNR) at the receiver. We thus propose a scheme that uses a pre-calibrated SNR-PSR relationship and instantaneous SNR estimates to calculate the PSR of the link. In our scheme, each receiver continuously tracks the SNR using a Kalman Filter to minimize the estimation error and uses a locally available SNR- PSR curve to estimate the PSR. Through extensive experiments we demonstrate that our scheme adapts to variations in the channel faster than counting-based PSR estimators and that it also provides better PSR estimates than these counting-based approaches.

Dhwani: secure peer-to-peer acoustic NFC

Near Field Communication (NFC) enables physically proximate devices to communicate over very short ranges in a peer-to-peer manner without incurring complex network configuration overheads. However, adoption of NFC-enabled applications has been stymied by the low levels of penetration of NFC hardware. In this paper, we address the challenge of enabling NFC-like capability on the existing base of mobile phones. To this end, we develop Dhwani, a novel, acoustics-based NFC system that uses the microphone and speakers on mobile phones, thus eliminating the need for any specialized NFC hardware. A key feature of Dhwani is the JamSecure technique, which uses self-jamming coupled with self-interference cancellation at the receiver, to provide an information-theoretically secure communication channel between the devices. Our current implementation of Dhwani achieves data rates of up to 2.4 Kbps, which is sufficient for most existing NFC applications.