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A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

Ahmad F. Subahi*
Department of Computer Science, University College of Al Jamoum, Umm Al Qura University, Makkah, 21421, Saudi Arabia
* Corresponding Author: Ahmad F. Subahi. Email:

Computer Systems Science and Engineering 2021, 36(1), 13-39. https://doi.org/10.32604/csse.2021.014376

Received 17 September 2020; Accepted 24 October 2020; Issue published 23 December 2020

Abstract

This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places. A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design. A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design. A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries, expressed using Neo4j Cypher, to provide insights from the stored data for decision support. As proof of concept, a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture. Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected. The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.

Keywords

Model-driven engineering (MDE); Internet-of-Things (IoTs); model transformation; edge computing; system design; Neo4j graph databases

Cite This Article

A. F. Subahi, "A model transformation approach for detecting distancing violations in weighted graphs," Computer Systems Science and Engineering, vol. 36, no.1, pp. 13–39, 2021.

Citations




This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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