Instrumentation of an environment with sensors can provide an effective and scalable localization solution for robots. Where GPS is not available, beacons that provide position estimates to a robot must be placed effectively in order to maximize a robots navigation accuracy and robustness. Sonar range-based beacons are reasonable candidates for low cost position estimate sensors. In this paper we explore heuristics derived from computational geometry to estimate the effectiveness of sonar beacon deployments given a predefined mobile robot path. Results from numerical simulations and experimentation demonstrate the effectiveness and scalability of our approach.
In this paper, we present an algorithm for simultaneously refining a probability distribution function (PDF) for the pose of a sensor network (i.e. the locations of the sensors), and inferring the spatial variations of measured environmental parameters. Our approach iteratively refines a network pose PDF by assuming that environmental parameters vary smoothly. Both our physical experiments, which sensed wireless signal strength as the environmental variable, and our numerical simulations demonstrate that the approach has promise.
A summary is presented of the paging activity observed for various programs executing on a System/370 model 145 using OS/VSI (Release 2.0). Paging activity was measured by periodic sampling of the queues involved in real storage page management and by inspection of page traffic counters maintained by the operating system.
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