D3.6 – IoT Data Pre-processing Module v2
Creators: name: Papafragkakis, Apostolos
name: Giannakis, Giorgos
name: Vasiliadis, Michalis
name: Katsigarakis, Kyriakos
name: Lillis, Georgios
name: Rovas, Dimitrios
name: Teizer, Jochen
name: Chronopoulos, Christos
name: Bosché, Frédéric
name: Esposito, Bueno Martín
name: Falcioni, Damiano
name: Andriopoulos, Panos
name: Triantafillou, Giannis
name: Koletsas, Giorgos
name: Straka, Martin
name: Archontidis, Alexandros
name: Papakostas, Konstantinos
Publisher: name: Zenodo
Year: 2022

description: The present deliverable aims at documenting the second version of the IoT Data Pre-Processing Module and consequently at reporting on the second iteration of COGITO Tasks T3.3 “IoT solution in Construction Phase“ and T3.4 “IoT data Pre-Processing module” design and development activities. In summary, the IoT Data Pre-processing Module is in charge of gathering raw IoT data coming from sensorial devices installed or worn at the construction site as well as pre-processing them and generating timeseries datasets that are ultimately fed to the COGITO Digital Twin platform. 

During the early stages of the project, several meetings were held among the COGITO partners to elicit the input requirements of COGITO applications that shall act as the main IoT data consumers. In the context of T3.3, using the MoSCoW (Must-Should-Could-Would have) prioritisation method, COGITO partners specified the requirements of the IoT solution that is envisaged, covering the respective input data to the Work Order Definition and Management (WODM), the Process Modelling and Simulation (PMS) and the ProactiveSafety applications of the COGITO architecture. Hitherto, resources tracking (on-site workers and heavy machinery) has been defined as the single IoT data input to those applications. Based on an initial list of the IoT solution’s requirements (e.g., accuracy, latency, sensing range, total cost of acquisition and deployment in pilot sites, and security), a survey of Real-Time Location Systems (RTLS) techniques and relevant technologies was conducted, contrasting their capability in capturing resources’ location while meeting those requirements. The results of that study indicated that Bluetooth 5.1 with Angle of Arrival (AoA) is a promising technology for indoor positioning/pathfinding that meets the COGITO requirements. Hence, it was selected as candidate for initial experimentation in a lab environment. A survey was also performed on well-known and well-established vendors that realise commercial solutions for short range wireless indoor positioning using Bluetooth 5.1 with AoA. Among them, Quuppa was found to fulfil COGITO needs, offering a development kit with comprehensive training for experimentation. The kit includes locators and tags along with a controller for running the provided software suite for planning, deploying, and configuring the system in a dedicated environment. The development kit was purchased, installed and tested at Hypertech’s premises (leading Tasks T3.3 and T3.4). Despite the fact that Quuppa was selected for the experimental setup, it is worth mentioning that the COGITO’s IoT Data Pre-processing Module is being developed as a technology-agnostic middleware capable of fusing the various RTLS technologies and techniques currently available in a unified and consistent manner. Hence, it provides a potential for enhancing accuracy by using multiple positioning systems and circumvents vendor lock-in.

Given the abovementioned IoT solution, the first version of the IoT Data Pre-processing Module was prototyped, capable of ingesting raw IoT data coming from the IoT solution, processing them and feeding them to the COGITO Digital Twin platform as documented in D3.5 “IoT Data Pre-Processing Module v1”. Since then, the module has undergone additions and changes based on actual experimentation, testing, bug identification and feedback received by other COGITO partners through both bilateral discussions and mini workshops. Such updates, along with the objectives, the sub-components, the technology stack and the implementation tools, the inputs/outputs and the exposed APIs of the IoT Data Pre-Processing Module are documented in this deliverable.