DEEP Hybrid DataCloud accepted paper in 9th International Conference on Ambient Systems, Networks and Technologies, May 8-11, 2018, Porto (Portugal)

Home » Scientific Publications » DEEP Hybrid DataCloud accepted paper in 9th International Conference on Ambient Systems, Networks and Technologies, May 8-11, 2018, Porto (Portugal)

We are proud to announce that a research paper developed under the Deep Hybrid DataCloud Project has been accepted for inclusion in 9th International Conference on Ambient Systems, Networks and Technologies to be held on 8-11 May 2018 in Porto (Portugal). This paper will be published by Elsevier Science in the open-access Procedia Computer Science series on-line.

Title: “An Information-centric Approach for Slice Monitoring from Edge Devices to Clouds

Authors: Binh Minh Nguyena, Huah Phana, Duong Quang Haa, Giang Nguyenb

a School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

b Institute of Informatics, Slovak Academy of Sciences, Bratislava 845 07 , Slovakia

ABSTRACT

Internet of Things (IoT) has enabled physical devices and virtual objects to be connected to share data, coordinate, and automatically make smart decisions to server people. Recently, many IoT resource slicing studies that allow managing devices, IoT platforms, network functions, and clouds under a single unified programming interface have been proposed. Although they helped IoT developers to create IoT services more easily, the efforts still have not dealt with the monitoring problem for the slice components. This could cause an issue: thing states could not be tracked continuously, and hence the effectiveness of controlling the IoT components would be decreased significantly because of updated information lack. In this paper, we introduce an information-centric approach for multiple sources monitoring issue in IoT. The proposed model thus is designed to provide generic and extensible data format for diverse IoT objects. Through this model, IoT developers can build smart services smoothly without worrying about the diversity as well as heterogeneity of collected data. We also propose an overall monitoring architecture for the information-centric model to deploy in IoT environment and its monitoring API prototype. This document also presents our experiments and evaluations to prove feasibility of the proposals in practice.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

Unless otherwise indicated, all materials created by the DEEP-Hybrid-DataCloud consortium are licensed under a Creative Commons Attribution 4.0 International License.

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