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Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes

Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes
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 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.
109-120
Osborne, Michael A
bc38034c-f714-4fb0-94a3-de66713452f5
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Roberts, Stephen J
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Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Osborne, Michael A
bc38034c-f714-4fb0-94a3-de66713452f5
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Roberts, Stephen J
583ce73c-02ef-4b75-9efe-8c1efa9aab9e
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Osborne, Michael A, Rogers, Alex, Ramchurn, Sarvapali, Roberts, Stephen J and Jennings, N. R. (2008) Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes. International Conference on Information Processing in Sensor Networks (IPSN 2008), United States. pp. 109-120 .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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More information

Published date: April 2008
Additional Information: Event Dates: April 2008
Venue - Dates: International Conference on Information Processing in Sensor Networks (IPSN 2008), United States, 2008-04-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 265122
URI: https://eprints.soton.ac.uk/id/eprint/265122
PURE UUID: f9c7e388-3775-42c1-a611-524d08e4ea78
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 29 Jan 2008 13:48
Last modified: 17 Jul 2019 00:52

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