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IoT-CANE: A unified knowledge management system for data-centric Internet of Things application systems
- Source :
- Journal of Parallel and Distributed Computing. 131:161-172
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Identifying a suitable configuration of devices, software and infrastructures in the context of user requirements is fundamental to the success of delivering IoT applications. As possible configurations could be large in number and not all configurations are valid, a configuration knowledge representation model can provide ready-made configurations based on IoT requirements. Combining such a model within the context of a given user-oriented scenario, it is possible to automate the recommendation of solutions for deployment and long-time evolution of IoT applications. However, in the context of Cloud/Edge technologies, that may themselves exhibit significant configuration possibilities that are also dynamic in nature, a more unified approach is required. We present IoT-CANE (Context Aware recommendatioN systEm) as such a unified approach. IoT-CANE embodies a unified conceptual model capturing configuration, constraint and infrastructure features of Cloud/Edge together with IoT devices. The success of IoT-CANE is evaluated through an end-user case study.
- Subjects :
- Knowledge representation and reasoning
Computer Networks and Communications
Computer science
Distributed computing
media_common.quotation_subject
Cloud computing
Context (language use)
02 engineering and technology
Ripple-down rules
User requirements document
Database-centric architecture
Theoretical Computer Science
Configuration Management (ITSM)
Software
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
media_common
Configuration management
business.industry
020206 networking & telecommunications
Hardware and Architecture
Software deployment
Conceptual model
020201 artificial intelligence & image processing
business
Subjects
Details
- ISSN :
- 07437315
- Volume :
- 131
- Database :
- OpenAIRE
- Journal :
- Journal of Parallel and Distributed Computing
- Accession number :
- edsair.doi...........7421b9683e6a4c8ecf430ba238bbfab4
- Full Text :
- https://doi.org/10.1016/j.jpdc.2019.04.016