Joshua Lifton

Sensor systems for interactive surfaces

This paper describes four different systems that we have developed for capturing various manners of gesture near interactive surfaces. The first is a low-cost scanning laser rangefinder adapted to accurately track the position of bare hands in a plane just above a large projection display. The second is an acoustic system that detects the position of taps on a large, continuous surface (such as a table, wall, or window) by measuring the differential time-of-arrival of the acoustic shock impulse at several discrete locations. The third is a sensate carpet that uses a grid of piezoelectric wire to measure the dynamic location and pressure of footfalls. The fourth is a swept radio frequency (RF) tag reader that measures the height, approximate location, and other properties (orientation or a control variable like pressure) of objects containing passive, magnetically coupled resonant tags, and updates the continuous parameters of all tagged objects at 30 Hz. In addition to discussing the technologies and surveying different approaches, sample applications are given for each system.

Localizing a sensor network via collaborative processing of global stimuli

In order for nodes in a sensor network to meaningfully correlate their sensor readings, they must first determine their position in a globally shared coordinate system. Though there are many approaches which are suitable for achieving localization in the general case, sensor nodes are uniquely suited to use their sensing capabilities to aid them in this task. Global events which are detected in the environment surrounding the sensor network can serve as points of correspondence which, through collaborative processing on the network, provide nodes with sufficient information to compute their position. We have implemented an algorithm based on this approach in the Pushpin Computing sensor network: a dense, 55 node network which is spread over an area of 0.5 square meters. By queuing off of the minimum number of ultrasound pulses and light flashes needed to determine 2D coordinates using a simple lateration approach, we show that nodes in the Pushpin network can compute their position with an average error of 5-cm and a error standard deviation of 3-cm. In this paper we present this localization system and characterize its accuracy in our hardware testbed.

Localization on the pushpin computing sensor network using spectral graph drawing and mesh relaxation

This work approaches the problem of localizing the nodes of a distributed sensor network by leveraging distance constraints such as inter-node separations or ranges between nodes and a globally observed event. Previous work has shown this problem to suffer from false minima, mesh folding, slow convergence, and sensitivity to initial position estimates. Here, we present a localization system that combines a technique known as spectral graph drawing (SGD) for initializing node position estimates and a standard mesh relaxation (MR) algorithm for converging to finer accuracy. We describe our combined localization system in detail and build on previous work by testing these techniques with real 40-kHz ultrasound time-of-flight range data collected from 58 nodes in the Pushpin Computing network, a dense hardware testbed spread over an area of one square meter. In this paper, we discuss convergence characteristics, accuracy, distributability, and the robustness of this localization system.

Experiences and directions in pushpin computing

Over the last three years we have built and experimented with the Pushpin computing wireless sensor network platform. The Pushpin platform is a tabletop multihop wireless sensor network testbed comprised of 100 nodes arbitrarily placed within a one-square-meter area. The Pushpin platforms concise form factor and extreme node density allow for fine-grained control of its environment and immediate user interaction, thereby uniquely situating it between simulated and real world sensor networks. This paper details our salient successes and lessons learned along the way. We also discuss how these experiences have shaped our vision of the future of wireless sensor networks and some concrete research directions to follow.

Media matrix: self-organizing distributed physical database

We introduce Media Matrix, a system applying distributed, embedded computing techniques to the creation and maintenance of a queriable database of physical objects such as compact discs, video cassettes, books, and component bins. This paper provides theory, design, and implementation details as well as future work and potential applications.