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 middle-ware 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.