Planning Tools for Large Scale Location Tracking: Difference between revisions

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Creating Planning tools for Large Scale Location Tracking
Client: Pete Steggles, [[Ubisense]] <Pete.Steggles@ubisense.net>


Client: Andy Ward, [[Ubisense]] <Andy.Ward@ubisense.net>
Our sensor system (https://www.ubisensedimension4.com) can be used to locate tools and cars on production lines, storing measurements and derived locations for audit purposes. Every day each factory generates ~2e8 locations from ~1e9 raw measurements.  There is an environment-dependent function from tag-to-sensor distance/bearing to sensor measurement probability/error, and an environment-independent function from a set of sensor measurements/errors to the probability of a ‘good’ tag location. Your task is to  use the stored data to characterize these functions, compare them across sites, and build a planning tool to optimize future installations.
 
This project uses large-scale data to optimize sensor networks.  Our sensor system (https://www.ubisensedimension4.com) can be used to locate tools and cars on production lines (e.g. https://www.youtube.com/watch?v=6UBhGaxhORo).  Measurements and derived locations are stored for audit purposes; every day each factory generates ~2e8 locations from ~1e9 raw measurements.  There is an environment-dependent function from tag-to-sensor distance/bearing to sensor measurement probability/error, and an environment-independent function from a set of sensor measurements/errors to the probability of a ‘good’ tag location. You will use the stored data to characterize these functions, compare them across sites, and build a planning tool to optimize future installations.

Latest revision as of 07:26, 23 January 2020

Client: Pete Steggles, Ubisense <Pete.Steggles@ubisense.net>

Our sensor system (https://www.ubisensedimension4.com) can be used to locate tools and cars on production lines, storing measurements and derived locations for audit purposes. Every day each factory generates ~2e8 locations from ~1e9 raw measurements. There is an environment-dependent function from tag-to-sensor distance/bearing to sensor measurement probability/error, and an environment-independent function from a set of sensor measurements/errors to the probability of a ‘good’ tag location. Your task is to use the stored data to characterize these functions, compare them across sites, and build a planning tool to optimize future installations.