Crowdsensing and Trusted Digital Twins for Environmental Noise Monitoring

This paper introduces an innovative Mobile Crowd Sensing (MCS) system and the related architecture to aggregate data with the goal of monitoring the noise within an indoor environment. Two of the most common problems with MCS systems are related to measurement localization and their trustworthiness. While GPS data is commonly used for outdoor MCS tasks, indoor environments present challenges for location-based measurements. In this sense, the proposed system makes use of Bluetooth beaconing to identify the rooms, while students’ smartphones act as sensors for noise evaluation. Moreover, to ensure data reliability and weed out malicious contributions by students, a trust management system is implemented, isolating users with anomalous measurements without completely excluding them from participation. The proposed solution revolves around the concept of digital twins (DTs), where physical objects and individuals are represented virtually. The key contributions of the research include the development of a crowdsensing system for the monitoring of environmental noise through trusted digital twins and a performance analysis conducted in a real-world scenario involving three university offices.