Nissanka Arachchige Bodhi Priyantha

Bio: Nissanka Arachchige Bodhi Priyantha is an academic researcher from Microsoft. The author has contributed to research in topics: Wireless sensor network & Battery (electricity). The author has an hindex of 27, co-authored 69 publications receiving 8681 citations. Previous affiliations of Nissanka Arachchige Bodhi Priyantha include Massachusetts Institute of Technology & Yale University.

The Cricket location-support system

Abstract: This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.

The cricket compass for context-aware mobile applications

Abstract: The ability to determine the orientation of a device is of fundamental importance in context aware and location-dependent mobile computing. By analogy to a traditional compass, knowledge of orientation through the Cricket compass attached to a mobile device enhances various applications, including efficient way-finding and navigation, directional service discovery, and “augmented-reality” displays. Our compass infrastructure enhances the spatial inference capability of the Cric ketindoor location system [20], and enables new pervasive computing applications.Using fixed active beacons and carefully placed passive ultrasonic sensors, we show how to estimate the orientation of a mobile device to within a few degrees, using precise, sub-centimeter differences in distance estimates from a beacon to each sensor on the compass. Then, given a set of fixed, active position beacons whose locations are known, we describe an algorithm that combines several carrier arrival times to produce a robust estimate of the rigid orientation of the mobile compass.The hardware of the Cricket compass is small enough to be integrated with a handheld mobile device. It includes five passive ultrasonic receivers, each 0.8cm in diameter, arrayed in a “V” shape a few centimeters across. Cricket beacons deployed throughout a building broadcast coupled 418MHz RF packet data and a 40KHz ultrasound carrier, which are processed by the compass software to obtain differential distance and position estimates. Our experimental results show that our prototype implementation can determine compass orientation to within 3 degrees when the true angle lies between ±30 degrees, and to within 5 degrees when the true angle lies between ±40 degrees, with respect to a fixed beacon.

Anchor-Free Distributed Localization in Sensor Networks

Abstract: Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.

The cricket indoor location system

Abstract: Indoor environments present opportunities for a rich set of location-aware applications such as navigation tools for humans and robots, interactive virtual games, resource discovery, asset tracking, location-aware sensor networking etc Typical indoor applications require better accuracy than what current outdoor location systems provide Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments Further, typical indoor applications require different types of location information such as physical space, position and orientation This dissertation describes the design and implementation of the Cricket indoor location system that provides accurate location in the form of user space, position and orientation to mobile and sensor network applications Cricket consists of location beacons that are attached to the ceiling of a building, and receivers, called listeners, attached to devices that need location Each beacon periodically transmits its location information in an RF message At the same time, the beacon also transmits an ultrasonic pulse The listeners listen to beacon transmissions and measure distances to nearby beacons, and use these distances to compute their own locations This active-beacon passive-listener architecture is scalable with respect to the number of users, and enables applications that preserve user privacy This dissertation describes how Cricket achieves accurate distance measurements between beacons and listeners Once the beacons are deployed, the MAT and AFL algorithms, described in this dissertation, use measurements taken at a mobile listener to configure the beacons with a coordinate assignment that reflects the beacon layout This dissertation presents beacon interference avoidance and detection algorithms, as well as outlier rejection algorithms to prevent and filter out outlier distance estimates caused by uncoordinated beacon transmissions The Cricket listeners can measure distances with an accuracy of 5 cm The listeners can detect boundaries with an accuracy of 1 cm Cricket has a position estimation accuracy of 10 cm and an orientation accuracy of 3 degrees (Copies available exclusively from MIT Libraries, Rm 14-0551, Cambridge, MA 02139-4307 Ph 617-253-5668; Fax 617-253-1690)

Tracking moving devices with the cricket location system

Abstract: We study the problem of tracking a moving device under two indoor location architectures: an active mobile architecture and a passive mobile architecture. In the former, the infrastructure has receivers at known locations, which estimate distances to a mobile device based on an active transmission from the device. In the latter, the infrastructure has active beacons that periodically transmit signals to a passively listening mobile device, which in turn estimates distances to the beacons. Because the active mobile architecture receives simultaneous distance estimates at multiple receivers from the mobile device, it is likely to perform better tracking than the passive mobile system in which the device obtains only one distance estimate at a time and may have moved between successive estimates. However, an passive mobile system scales better with the number of mobile devices and puts users in control of whether their whereabouts are tracked.We answer the following question: How do the two architectures compare in tracking performance? We find that the active mobile architecture performs better at tracking, but that the passive mobile architecture has acceptable performance; moreover, we devise a hybrid approach that preserves the benefits of the passive mobile architecture while simultaneously providing the same performance as an active mobile system, suggesting a viable practical solution to the three goals of scalability, privacy, and tracking agility.

Location systems for ubiquitous computing

Abstract: This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field. To make sense of this domain, we have developed a taxonomy to help developers of location-aware applications better evaluate their options when choosing a location-sensing system. The taxonomy may also aid researchers in identifying opportunities for new location-sensing techniques.

Dynamic fine-grained localization in Ad-Hoc networks of sensors

Abstract: The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

LANDMARC: indoor location sensing using active RFID

Abstract: Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of "context-aware" applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.