Thesis Presentations Group 5

Title: Modeling of Messaging System for IoT Enabled Waste Management System
Author: Kugblenu, Carl
Supervisor(s): Prof. Arkady Zaslavsky, Dr. Sylvain Kubler
Hosting Institution: ITMO University, St Petersburg, Russia
Abstract: Insert thesis abstract(200 words)
Paper: Insert link to thesis paper.

Title: Budget of LPWAN Architectures
Author: Rady, Mina
Supervisor(s): Professor Francis Lepage; Dr. Jean-Philippe Georges
Hosting Institution: Center for Research and Automatic Control of Nancy
Abstract: In this paper, we propose a model for the total budget of IoT LPWAN architectures to estimate their real economic and environmental costs. Based on a systems engineering view of an IoT sensor node we provide a comprehensive model that estimates the total operational expenditure (OpEx) of generally any network of sensor nodes while taking into account the variation in technological parameters. We also show that non-radio components may interfere with network Quality of Service (QoS) and we provide verified theoretical framework for accurately predicting and controlling Internet of Things (IoT) node behavior. We provide an optimization model that is guaranteed to find least OpEx-expensive link assigned in an LPWAN IoT connected-star topology with heterogeneous End Device (ED) configurations. We also show that significant budget and environmental hazardous waste savings can be achieved through seemingly passive network changes such as introducing few gateways (GWs) or removing an unneeded timestamp from packet payload.
Paper: Insert link to thesis paper.

Title: Machine learning assisted system for the resource-constrained atrial fibrillation detection from short single-lead ECG signals
Author: Abdukalikova, Anara
Supervisor(s): Professor Evgeny Osipov; Denis Kleyko
Hosting Institution: Luleå University of Technology
Abstract: An integration of ICT advances into a conventional healthcare system is spreading extensively nowadays. This trend is known as Electronic health or E-Health. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. Cardiovascular diseases are one of the main killers yearly causing approximately 17.7 million deaths worldwide. The focus of this work is on studying the detection of one of the potential causes of cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia. This type of arrhythmia has a severe influence on the heart health conditions and could cause congestive heart failure (CHF), stroke, and even increase the risk of death. Therefore, it is important to detect AF as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using only short single lead Electrocardiography recordings. A web-based solution is proposed as a final prototype, which first simulates the reception of signal, conducts the analysis, makes a prediction of the AF presence, and visualizes the result. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was taken into account during the development phase of the project.
Paper: Insert link to thesis paper.