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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: Communication between Internet of Things (IoT), services and humans are a very important task for future smart cities. Connecting of thousands of services, millions of people and billions of smart devices in modern cities is a major problem. Unifying of messaging model may radically simplify this process, especially during the adoption of new components and sharing of information. In this paper, we investigate messaging standards in IoT highlighting Open Group’s O-MI/O-DF messaging standard. A comprehensive performance evaluation is performed, and the results discussed. A smart waste management use case is developed and message simulation tool with O-MI/O-DF as the communication layer
Paper: Insert link to thesis paper.

Title: Budget of IoT 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: 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.

Author: Heathcote-Fumador, Ida
Supervisor(s): Prof. Jari Porras, Prof. Karan Mitra, and Prof. Saguna Saguna
Hosting Institution: Lappeenranta and Luleå University of Technology
Abstract: It is established that the success of sustainable projects is greatly linked to meeting current human needs. Sufficient evidence discovered to proof that Decision Support System can be used to select the most useful and likely to be successful projects. In this paper, open data available for countries has been used to develop a decision support system for smart decision making in countries. It is confirmed Analytical Hierarchy Process’s consistency index can be improved using our relative importance calculator and distance calculator.
Paper: Insert link to thesis paper.

Title: Data Center Performance Comparison Framework based on Biomimicry
Author: Mutua, Phoebe
Supervisor(s): Prof. Eric Rondeau, Dr. Jean-Philippe Georges, and Dr. Sylvain Kubler
Hosting Institution: Center for Research and Automatic Control of Nancy, University of Lorraine
Abstract: Nature’s competence and its harmonious coexistence for over 3.8 billion years, provides us with models which can transform our consumption of resources, waste management and environmental conservation in a rational and sustainable manner. The increase and demand for fast, efficient data processing and storage services coupled with the high energy usage necessitate the need for changes in the design and operations of the data center for improved performance. To efficiently appraise data centers, we must broadly examine all the components that make up the composition of a data center. There are a collection of data center metrics available today for assessing data centers which can be categorized into energy efficiency metrics, green metrics, cooling metrics, performance metrics, storage metrics, network metrics, security metrics and financial metrics. The complexity presented from analyzing metrics of such magnitude can be difficult to decipher. Therefore, this research assesses the sustainability of data centers by analyzing the different data center metrics with respect to biomimicry rules and using Analytic Hierarchy Process to showcase in a simple understandable manner, the holistic global view of the data center’s performance which is beneficial for continuous improvements and informed decision making for future scaling of services.
Paper: Insert link to thesis paper.