The process of determining the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire inter-node distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1 % (33cm). The localization algorithm is based on Least Squares Scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multi-dimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor

In this paper, we address the problem of tracking cooperative mobile nodes in wireless sensor networks. Aiming at a resource efficient solution, we advocate the use of sensors that maintain their location information and rely on the tracking service only when their locations change. In the proposed approach, the tracked node transmits a signal and infrastructure nodes measure the Doppler shifts of the transmitted signal. We show that Mica2 motes can measure RF Doppler shifts with 0.2 Hz accuracy corresponding to a 0.14 m/s error in relative speed estimates using radio interferometric technique. The tracking problem is modeled as a non-linear optimization problem and an extended Kalman filter is used to solve it accurately assuming Gaussian measurement errors. However, this approach may fail if the tracked node changes its speed or direction. We propose to update the Kalman filter state by performing constrained least-squares optimization when a maneuver is detected. The combined approach achieves almost a 50 % accuracy improvement over the Kalman filter alone when the mobile node changes its direction and speed frequently. We describe our proof-ofconcept implementation of the tracking service and evaluate its performance experimentally and in simulation.

We present the design, implementation, and evaluation of BeepBeep, a high-accuracy acoustic-based ranging system. It operates in a spontaneous, ad-hoc, and device-to-device context without leveraging any pre-planned infrastructure. It is a pure software-based solution and uses only the most basic set of commodity hardware – a speaker, a microphone, and some form of device-to-device communication – so that it is readily applicable to many low-cost sensor platforms and to most commercial-off-the-shelf mobile devices like cell phones and PDAs. It achieves high accuracy through a combination of three techniques: two-way sensing, selfrecording, and sample counting. The basic idea is the following. To estimate the range between two devices, each will emit a specially-designed sound signal (“Beep”) and collect a simultaneous recording from its microphone. Each recording should contain two such beeps, one from its own speaker and the other from its peer. By counting the number of samples between these two beeps and exchanging the time duration information with its peer, each device can derive the two-way time of flight of the beeps at the granularity of sound sampling rate. This technique cleverly avoids many sources of inaccuracy found in other typical time-of-arrival schemes, such as clock synchronization, non-real-time handling, software delays, etc. Our experiments on two common cell phone models have shown that we can achieve around one or two centimeters accuracy within a range of more than ten meters, despite a series of technical challenges in implementing the idea.

Location-awareness is an important requirement for many mobile wireless applications today. When GPS is not applicable because of the required precision and/or the resource constraints on the hardware platform, radio interferometric ranging may offer an alternative. In this paper, we present a technique that enables the precise tracking of multiple wireless nodes simultaneously. It relies on multiple infrastructure nodes deployed at known locations measuring the position of tracked mobile nodes using radio interferometry. In addition to location information, the approach also provides node velocity estimates by measuring the Doppler shift of the interference signal. The performance of the technique is evaluated using a prototype implementation on mote-class wireless sensor nodes. Finally, a possible application scenario of dirty bomb detection in a football stadium is briefly described.

The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the localization phase. In this paper we propose a framework, called StarDust, for wireless sensor network localization based on passive optical components. In the StarDust framework, sensor nodes are equipped with optical retro-reflectors. An aerial device projects light towards the deployed sensor network, and records an image of the reflected light. An image processing algorithm is developed for obtaining the locations of sensor nodes. For matching a node ID to a location we propose a constraint-based label relaxation algorithm. We propose and develop localization techniques based on four types of constraints: node color, neighbor information, deployment time for a node and deployment location for a node. We evaluate the performance of a localization system based on our framework by localizing a network of 26 sensor nodes deployed in a 120 × 60ft 2 area. The localization accuracy ranges from 2ft to 5ft while the localization time ranges from 10 milliseconds to 2 minutes.

Wireless sensor networks have been proposed for many location-dependent applications. In such applications, the requirement of low system cost prohibitsmany range-based methods for sensor node localization; on the other hand, range-free localization depending only on connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to the above limitations, this paper presents a range-free approach to capturing a relative distancebetween1-hopneighboringnodesfromtheirneighborhood orderings that serve as unique high-dimensional location signatures for nodes in the network. With little overhead, the proposed design can be conveniently applied as a transparent supporting layer for many state-of-the-art connectivity-based localization solutions to achieve better positioningaccuracy. Weimplementedourdesignwiththree well-knownlocalizationalgorithmsandtesteditintwotypes ofoutdoortest-bedexperiments: an850-foot-longlinearnetwork with 54 MICAz motes, and a regular2D networkcovering an area of 10000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with sub-hop resolution, and reduces localization errors by as much as 35%. In addition, extensive simulations reveal an interestingfeature of robustnessfor our design underunevenly distributed radio propagation path loss, and confirm itseffectivenessforlarge-scalenetworks. Categories andSubject Descriptors

This paper presents an enhanced version of a novel radio interferometric positioning technique for node localization in wireless sensor networks that provides both high accuracy and long range simultaneously. The ranging method utilizes two transmitters emitting radio signals at almost the same frequencies. The relative location is estimated by measuring the relative phase offset of the generated interference signal at two receivers. Here, we analyze how the selection of carrier frequencies affects the precision and maximum range. Furthermore, we describe how the interplay of RF multipath and ground reflections degrades the ranging accuracy. To address these problems, we introduce a technique that continuously refines the range estimates as it converges to the localization solution. Finally, we present the results of a field experiment where our prototype achieved 4 cm average localization accuracy for a quasi-random deployment of 16 COTS motes covering the area of two football fields. The maximum range measured was 170 m, four times the observed communication range. Consequently, node deployment density is no longer constrained by the localization technique, but rather by the communication range.

The node localization problem in Wireless Sensor Networks has received considerable attention, driven by the need to obtain a higher location accuracy without incurring a large, per node, cost (dollar cost, power consumption and form factor). Despite the efforts made, no system has emerged as a robust, practical, solution for the node localization problem in realistic, complex, outdoor environments. In this paper, we argue that the existing localization algorithms, individually, work well for single sets of assumptions. These assumptions do not always hold, as in the case of outdoor, complex environments. To solve this problem, we propose a framework that allows the execution of multiple localization schemes. This “protocol multi-modality ” enables robustness against any single protocol failure, due to its assumptions. We present the design of the framework, and show a 50% decrease in localization error in comparison with state of art node localization protocols. We also show that complex, more robust, localization systems can be build from localization schemes that have limitations.

We propose SecNav, a new protocol for securing wireless navigation systems. This protocol secures localization and time-synchronization in wireless networks by relying on devices ’ awareness of presence in the power-range (coverage area) of navigation stations. We perform a detailed security analysis of SecNav and show that, compared to existing secure navigation approaches, it prevents the widest range of attacks on navigation. Our implementation of SecNav, using 802.11b devices, shows that this scheme can be efficiently implemented with existing technologies. 1

Abstract. Radio-interferometric ranging is a novel technique that allows for fine-grained node localization in networks of inexpensive COTS nodes. In this paper, we show that the approach can also be applied to precision tracking of mobile sensor nodes. We introduce inTrack, a cooperative tracking system based on radio-interferometry that features high accuracy, long range and low-power operation. The system utilizes a set of nodes placed at known locations to track a mobile sensor. We analyze how target speed and measurement errors affect the accuracy of the computed locations. To demonstrate the feasibility of our approach, we describe our prototype implementation using Berkeley motes. We evaluate the system using data from both simulations and field tests. 1