Intelligent resource selection for sensor-task assignment : a knowledge-based approach
Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dyn...
Main Author: | |
---|---|
Published: |
University of Aberdeen
2014
|
Subjects: | |
Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629409 |
id |
ndltd-bl.uk-oai-ethos.bl.uk-629409 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-bl.uk-oai-ethos.bl.uk-6294092016-08-04T03:59:32ZIntelligent resource selection for sensor-task assignment : a knowledge-based approachDe Mel, Geeth R.2014Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks. Though a considerable amount of research has already been done in different communities to efficiently allocate resources to tasks, we argue that there is little work done to guarantee the effectiveness of the section with respect to the context of operation. In this thesis, we propose a knowledge-based approach in which the context of operation is introduced to the resource selection process. First, we present a formalism to represent a sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources; a query language – and its semantics – to discover appropriate service collections for user requirements are also presented. We have evaluated our work through controlled experiments and critical arguments. Through these evaluations, we have shown that our approach can indeed improve the resource selection process and can augment resource allocation mechanisms. Our approach is general in that, it can be applied in many other domains.004Intelligent sensorsUniversity of Aberdeenhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629409http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=215104Electronic Thesis or Dissertation |
collection |
NDLTD |
sources |
NDLTD |
topic |
004 Intelligent sensors |
spellingShingle |
004 Intelligent sensors De Mel, Geeth R. Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
description |
Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks. Though a considerable amount of research has already been done in different communities to efficiently allocate resources to tasks, we argue that there is little work done to guarantee the effectiveness of the section with respect to the context of operation. In this thesis, we propose a knowledge-based approach in which the context of operation is introduced to the resource selection process. First, we present a formalism to represent a sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources; a query language – and its semantics – to discover appropriate service collections for user requirements are also presented. We have evaluated our work through controlled experiments and critical arguments. Through these evaluations, we have shown that our approach can indeed improve the resource selection process and can augment resource allocation mechanisms. Our approach is general in that, it can be applied in many other domains. |
author |
De Mel, Geeth R. |
author_facet |
De Mel, Geeth R. |
author_sort |
De Mel, Geeth R. |
title |
Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
title_short |
Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
title_full |
Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
title_fullStr |
Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
title_full_unstemmed |
Intelligent resource selection for sensor-task assignment : a knowledge-based approach |
title_sort |
intelligent resource selection for sensor-task assignment : a knowledge-based approach |
publisher |
University of Aberdeen |
publishDate |
2014 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629409 |
work_keys_str_mv |
AT demelgeethr intelligentresourceselectionforsensortaskassignmentaknowledgebasedapproach |
_version_ |
1718372301003554816 |