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dc.contributor.authorLiu, Lu
dc.contributor.authorAntonopoulos, Nikolaos
dc.contributor.authorZheng, Minghui
dc.contributor.authorZhan, Yongzhao
dc.contributor.authorDing, Zhijun
dc.date.accessioned2016-11-15T10:54:39Z
dc.date.available2016-11-15T10:54:39Z
dc.date.issued2016-03-11
dc.identifier.citationLiu, L. et al, (2016) 'A Socioecological Model for Advanced Service Discovery in Machine-to-Machine Communication Networks' , ACM Transactions on Embedded Computing Systems, 15 (2):1. DOI: 10.1145/2811264en
dc.identifier.issn15399087
dc.identifier.doi10.1145/2811264
dc.identifier.urihttp://hdl.handle.net/10545/620846
dc.description.abstractThe new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=2888407.2811264en
dc.rightsArchived with thanks to ACM Transactions on Embedded Computing Systemsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectSocial-ecological modelen
dc.subjectService discoveryen
dc.subjectMachine-to-machine communication networksen
dc.titleA socioecological model for advanced service discovery in machine-to-machine communication networksen
dc.typeArticleen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalACM Transactions on Embedded Computing Systemsen
dc.contributor.institutionUniversity of Derby, Derby, UK
dc.contributor.institutionUniversity of Derby, Derby, UK
dc.contributor.institutionHubei University for Nationalities, Enshi, China
dc.contributor.institutionJiangsu University, Zhenjiang, China
dc.contributor.institutionTongji University, Shanghai, China
dcterms.dateAccepted2015-07-01
html.description.abstractThe new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.


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