The Cricket Location-Support System

This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, locationdependent 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.

Anchor-Free Distributed Localization in Sensor Networks

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

an Xforms Approach

access for mobile, sensor-enhanced web clients such as wireless cameras or wireless PDAs with sensor devices attached. The clients announce their data-creating capabilities in "Produce" headers sent to servers; servers respond with forms that match these capabilities. Clients fill in these forms with sensor data as well as text or file data. The resultant system enables clients to access dynamically discovered services spontaneously, as their users engage in everyday nomadic activities

The cricket compass for context-aware mobile applications

The abilit y to determine the orien tation of a device is of fundamental importancein con text-aw areand location-dependent mobile computing. By analogy to a traditional compass, knowledge of orientation through the Cricket c om-pass attached to a mobile device enhances various applica-tions, including eÆcient way-nding and navigation, direc-tional service disco very,and \augmented-realit y " displays. Our compass infrastructure enhances the spatial inference capabilit yof the Cric ketindoor location system [20], and enables new pervasiv e computing applications. Using xed active beacons and carefully placed passive ul-trasonic sensors, w e sho whow to estimate the orien tation of a mobile device to within a few degrees, using precise, sub-cen timeter dierences in distance estimates from a bea-con to each sensor on the compass. Then, given a set of xed, active position beacons whose locations are known, we describe an algorithm that combines sev eral carrier arriv al 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 ve passiv e ultrasonic receivers, each 0.8cm in diameter, arrayed in a \V " shape a few centimeters across. Cric ket beacons deplo yed throughout a building broadcast coupled 418MHz RF packet data and a 40KHz ultrasound carrier, which are processed by the compass softw are to obtain dierential dis-tance and position estimates. Our experimental results sho w that our prototype implementation can determine compass orien tation to within 3 degrees when the true angle lies be-tween 30 degrees, and to within 5 degrees when the true angle lies between 40 degrees, with respect to a xed bea-con. 1

Mobile-assisted localization in wireless sensor networks

Abstract — The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a “globally rigid ” structure that guarantees a unique localization. We derive the required constraints on the mobile’s movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile’s movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL’s median pairwise distance error is less than 1.5 % of the true node distance. I