A modified distributed bees algorithm for multi-sensor task allocation

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algor...

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Detalles Bibliográficos
Autores: Tkach, Itshak, Jevtic, Aleksandar, Nof, Shimon Y., Edan, Yael
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/115385
Acceso en línea:https://hdl.handle.net/2117/115385
https://dx.doi.org/10.3390/s18030759
Access Level:acceso abierto
Palabra clave:Swarm intelligence
multi-agent systems
multi-robot systems. swarm intelligence
Intel·ligència col·lectiva
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oai_identifier_str oai:upcommons.upc.edu:2117/115385
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repository_id_str
spelling A modified distributed bees algorithm for multi-sensor task allocationTkach, ItshakJevtic, AleksandarNof, Shimon Y.Edan, YaelSwarm intelligencemulti-agent systemsmulti-robot systems. swarm intelligenceIntel·ligència col·lectivaMulti-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors' performance, tasks' priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20182018-01-0120182018-03-19journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/115385https://dx.doi.org/10.3390/s1803075929498683reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1153852026-05-27T15:37:01Z
dc.title.none.fl_str_mv A modified distributed bees algorithm for multi-sensor task allocation
title A modified distributed bees algorithm for multi-sensor task allocation
spellingShingle A modified distributed bees algorithm for multi-sensor task allocation
Tkach, Itshak
Swarm intelligence
multi-agent systems
multi-robot systems. swarm intelligence
Intel·ligència col·lectiva
title_short A modified distributed bees algorithm for multi-sensor task allocation
title_full A modified distributed bees algorithm for multi-sensor task allocation
title_fullStr A modified distributed bees algorithm for multi-sensor task allocation
title_full_unstemmed A modified distributed bees algorithm for multi-sensor task allocation
title_sort A modified distributed bees algorithm for multi-sensor task allocation
dc.creator.none.fl_str_mv Tkach, Itshak
Jevtic, Aleksandar
Nof, Shimon Y.
Edan, Yael
author Tkach, Itshak
author_facet Tkach, Itshak
Jevtic, Aleksandar
Nof, Shimon Y.
Edan, Yael
author_role author
author2 Jevtic, Aleksandar
Nof, Shimon Y.
Edan, Yael
author2_role author
author
author
dc.subject.none.fl_str_mv Swarm intelligence
multi-agent systems
multi-robot systems. swarm intelligence
Intel·ligència col·lectiva
topic Swarm intelligence
multi-agent systems
multi-robot systems. swarm intelligence
Intel·ligència col·lectiva
description Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors' performance, tasks' priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01
2018
2018-03-19
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/115385
https://dx.doi.org/10.3390/s18030759
29498683
url https://hdl.handle.net/2117/115385
https://dx.doi.org/10.3390/s18030759
identifier_str_mv 29498683
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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