Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes
In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information pro...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference item |
| Published: |
2008
|
| _version_ | 1797065890915680256 |
|---|---|
| author | Osborne, M Roberts, S Rogers, A Ramchurn, S Jennings, N Soc, I |
| author_facet | Osborne, M Roberts, S Rogers, A Ramchurn, S Jennings, N Soc, I |
| author_sort | Osborne, M |
| collection | OXFORD |
| description | In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England. © 2008 IEEE. |
| first_indexed | 2024-03-06T21:34:49Z |
| format | Conference item |
| id | oxford-uuid:45dd4d3e-fa01-438a-9227-40d7c14e1e3b |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:34:49Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | oxford-uuid:45dd4d3e-fa01-438a-9227-40d7c14e1e3b2022-03-26T15:10:24ZTowards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:45dd4d3e-fa01-438a-9227-40d7c14e1e3bSymplectic Elements at Oxford2008Osborne, MRoberts, SRogers, ARamchurn, SJennings, NSoc, IIn this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England. © 2008 IEEE. |
| spellingShingle | Osborne, M Roberts, S Rogers, A Ramchurn, S Jennings, N Soc, I Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title | Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title_full | Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title_fullStr | Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title_full_unstemmed | Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title_short | Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes |
| title_sort | towards real time information processing of sensor network data using computationally efficient multi output gaussian processes |
| work_keys_str_mv | AT osbornem towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses AT robertss towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses AT rogersa towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses AT ramchurns towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses AT jenningsn towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses AT soci towardsrealtimeinformationprocessingofsensornetworkdatausingcomputationallyefficientmultioutputgaussianprocesses |