Towards Automated Service Composition Using Policy Ontology in Building Automation System

Han, Son N.; Lee, Gyu Myoung; Crespi, Noël

doi:10.1109/scc.2012.42

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…In [33], a service composition model that is restricted by policy ontology is presented. The composite service is created for only one user and contains multiple web services over physical devices.…”

Section: Related Workmentioning

confidence: 99%

Conflict Detection and Resolution Model for Multi-users IoT Automation Systems

2022

IJIES

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Building automation (BA) strives to control interconnected physical devices by using software management systems on which end-users can personalize their environmental preferences. In large buildings, among the leading causes of IoT apps conflict are the shareable locations/devices amongst residents and the diversity of their preferences. Addressing such conflicts and ensuring the safety of residents are vital requirements in building automation systems. Consequently, the potential of IoT safety and correctness frameworks relies on supporting conflict detection. This paper provides a model for detecting and resolving IoT automation conflicts. These conflicts can result from shareable locations or devices. The proposed model is evaluated using a benchmark dataset and refined scenarios collected from competitor-related works (80+ IoT apps with 117+ rules). The proposed model surpasses state-of-the-art models by covering more conflicts (joint behavior conflict); moreover, it does not require events' chain between IoT apps like other models, one more advantage is that the proposed model uses a filtering process in conflict detection which leads to small detection run-time. Thereby, our proposed model can maximize the correctness and safety of building automation systems.

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Section: Related Workmentioning

confidence: 99%

Conflict Detection and Resolution Model for Multi-users IoT Automation Systems

2022

IJIES

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“…Ontologies have been well researched with some modern examples found in [82]- [85]. At its core, an ontology is a graph, using graph theory to collate and organise information.…”

Section: Semantic Modeling Rdf/rdfs and Ontologies A Semantic mentioning

confidence: 99%

Semantically Modeling Cyber Influence Campaigns (CICs): Ontology Model and Case Studies

et al. 2021

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“…This choice is motivated by the possibilities it opens: -Applied to data, it is a way to bring context-awareness, as presented in [10] -Applied to devices, it enables Thing discovery or self-configuration [11] -Applied to services it enables automatic composition as in [12] However, for concrete applications, the model should also by decidable, and in reasonable time, which de facto excludes an OWL-full model: OWL-DL is therefore the best choice. All surveyed ontologies are expressed in OWL-DL.…”

Section: Level Of Formalismmentioning

confidence: 99%