@article{LI2019161,
title = "IoT-CANE: A unified knowledge management system for data-centric Internet of Things application systems",
journal = "Journal of Parallel and Distributed Computing",
volume = "131",
pages = "161 - 172",
year = "2019",
issn = "0743-7315",
doi = "https://doi.org/10.1016/j.jpdc.2019.04.016",
url = "http://www.sciencedirect.com/science/article/pii/S0743731518309742",
author = "Yinhao Li and Awatif Alqahtani and Ellis Solaiman and Charith Perera and Prem Prakash Jayaraman and Rajkumar Buyya and Graham Morgan and Rajiv Ranjan",
keywords = "Internet of Things, Knowledge representation, Recommender system, Ripple Down Rules, Configuration management",
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."
}