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Information Agents for Pervasive Sensor Networks

Information Agents for Pervasive Sensor Networks
Information Agents for Pervasive Sensor Networks
In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can 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 parameters 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 we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and 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.
294-299
Rogers, Alex
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Osborne, Michael A
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Ramchurn, Sarvapali
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Roberts, Stephen J
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Jennings, N. R.
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Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Osborne, Michael A
bc38034c-f714-4fb0-94a3-de66713452f5
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Roberts, Stephen J
583ce73c-02ef-4b75-9efe-8c1efa9aab9e
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Rogers, Alex, Osborne, Michael A, Ramchurn, Sarvapali, Roberts, Stephen J and Jennings, N. R. (2008) Information Agents for Pervasive Sensor Networks. Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008), China. pp. 294-299 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can 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 parameters 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 we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and 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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Published date: March 2008
Additional Information: Event Dates: March 2008
Venue - Dates: Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008), China, 2008-03-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 264967
URI: https://eprints.soton.ac.uk/id/eprint/264967
PURE UUID: d65d53ab-d683-4744-b4d1-788568a4061d
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 13 Dec 2007 11:18
Last modified: 20 Jul 2019 00:56

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Contributors

Author: Alex Rogers
Author: Michael A Osborne
Author: Sarvapali Ramchurn ORCID iD
Author: Stephen J Roberts
Author: N. R. Jennings

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