RESEARCH PROJECTS

RESEARCH PROJECTS

PERSEUS will recruit 40 doctoral candidates in total. Here you can meet the current PERSEUS PhD candidates


BIG DATA AND AI

BIG DATA AND AI

Artificial intelligence (AI) is at the center of the digital transformation of society. Value gains from this technology are already happening, and such gains are expected to dramatically increase in almost every domain through improved productivity, quality, and personalization of offerings. Data acts as the fuel for AI, while AI is needed to extract actionable information from huge data sets. Big data and AI are hence synergistically coupled to each other. The 10 PhD positions in this area will focus on challenges in the areas of energy informatics, healthcare, manufacturing (industry 4.0), mobility and the maritime sectors. They will be set up with strong multi-disciplinary collaboration opportunities linked to relevant NTNU led research centers. The planned ESRs research will aim to go beyond limited focus responses, e.g. by addressing the more complex integration of AI into hybrid solutions (e.g., by combining explicit models and ML tools, classical control theory and AI solutions); to enhance systems with self-adapting, online algorithms that work natively from imbalanced data; and to enable learning from complex data and producing complex outputs. 


Towards Generic Neural Network Search in Graph Neural Networks for Human Movement Analysis

Towards Generic Neural Network Search in Graph Neural Networks for Human Movement Analysis

PhD candidate: Felix Ernst Friedrich Tempel
Supervisor: Heri Ramampiaro
Co-supervisor:  Helge Langseth
Co-supervisor: Espen Alexander F. Ihlen

 

Felix will work in an R&D and innovation project named DeepInMotion funded by the Norwegian Research Council. The main objective of the project is to develop an explainable AI system and clinical service implementation to discover movement biomarkers for early detection of CP in infants. Felix will develop explainable AI techniques and methods that will contribute in generating new knSupervisorowledge and innovations, which will enable and provide decision support for early detection of motor disabilities in children. He will work in an interdisciplinary research group with researchers from Department of Neuromedicine and Movement Science, Department of Computer Science and clinics at St Olavs and Ålesund hosptitals.

 


Clinical Decision Support for Early Detection of Cerebral Palsy: Developing Post-hoc Explainable AI Methods to Discover Potential Biomarkers Through Video Analysis of Infant Movements

Clinical Decision Support for Early Detection of Cerebral Palsy: Developing Post-hoc Explainable AI Methods to Discover Potential Biomarkers Through Video Analysis of Infant Movements

PhD candidate: Kimji Pellano
Supervisor: Espen Alexander F. Ihlen
Co-supervisor: Heri Ramampiaro
Co-supervisor: Lars Adde

Kimji will work in R&D and innovation project named DeepInMotion founded by the Norwegian Research Council. The main objective of the project is to develop an explainable AI system and clinical service implementation to discover movement biomarkers for early detection of CP in infants. Kimji will contribute in the generation of new knowledge and techniques in the research area of explainable AI for early detection of motor disabilities in children for clinical decision support. He will be first author of at least 3 articles in international peer-review journals or proceedings and attain international conferences within the field of AI and biomedical engineering.

 


AI at work in Safety-critical Remote Operations

AI at work in Safety-critical Remote Operations

PhD candidate: Karthick Deivasagayam
Supervisor: Eric Monteiro
Co-supervisor: Vidar Hepsø
Co-supervisor: Elena Parmiggiani

 

In the energy industry (such as wind, oil and gas, utility), safety-critical remote operations are increasingly conducted from remote locations such as control rooms. Remote operations rely on real-time sensor data feeds that combine AI-based (meta)data visualization and detection systems to help operators identify the relevant information and make efficient decisions. Due to safety and reporting concerns, it is important that human operators remain in control. To ensure safe and efficient decision-making, there is a need to develop a better, in-depth understanding of these complex human-AI hybrids, as well as explore improved approaches to sensemaking and training based on e.g. Extended Reality (XR)-based serious games that help prevent the “data drowning” problem. The goal of Kathrick's PhD project is to contribute investigating how AI-infused remote operations can be implemented to improve decision-making processes in organizations. The research design is inspired by Design Science Research, in which empirical data collection will be alternated with prototyping and experiments to test the developed hypotheses. Kathrick will be involved in the Digital Enterprise strategic research area at the faculty of Information Technology and Electrical Engineering, and in the BRU21 multidisciplinary programme which will coordinate design and access to empirical cases and partners from the energy industry.

 

Developing Recommendations for Trustworthy Methods in AI Applications

Developing Recommendations for Trustworthy Methods in AI Applications

PhD candidate: Lena Jedamski
Supervisor: Kerstin Bach
Co-supervisor: Andreas Hafver
Co-supervisor:

 

Building trustworthy AI systems is a cornerstone to apply AI technologies in practice and therefore we need to explore methods, build tools, and incorporate different perspectives when developing novel AI applications. Based on the definition of the High Level Expert Group of the European Union, trustworthy AI should be lawful, ethical, and robust. While guidelines exist, research on implementation methodologies are currently under development and this research will contribute to develop principles for creating trustworthy AI applications. Together with partners in the NorwAI SFI Lena will work on the creation for guidelines for a sustainable and beneficial use of AI, explore privacy-preserving technologies and create explainable, interpretable and transparent prototypes to be tested in industrial settings.

 

BigData and AI for Future Mobility Solutions

BigData and AI for Future Mobility Solutions

PhD candidate: Oluwaleke Umar Yusuf
Supervisor: Adil Rasheed
Co-supervisor: Frank Lindseth
Co-supervisor:

 

The aim of the PhD will be to develop and exploit big data and artificial intelligence-driven digital twin of urban mobility infrastructures to solve challenges in achieving a carbon-neutral mobility future. A digital twin is defined as a virtual representation of a physical asset or process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and improved decision making. The PhD will work towards developing enabling technologies to instill physical realism in such a digital twin. The enabling technologies will consist of data acquisition, pre-processing, fusion, and postprocessing techniques using an array of physics-based, data-driven, and hybrid models. In addition, the work will also involve the development of tools for communicating insights in a way that facilitates informed public opinion building and decision-making.

 

Statistical Machine Learning in Distributed Acoustic Sensing to Automate Event Detections

Statistical Machine Learning in Distributed Acoustic Sensing to Automate Event Detections

PhD candidate: Khanh Truong
Supervisor: Jo Eidsvik
Co-supervisor: Robin André Rørstadbotnen
Co-supervisor: Jan Petter Morten

 

Khanh will be conducting statistical research on spatial and spatio-temporal processes that can be used for extracting relevant information from various types of geophysical data. The study focus of his PhD project is on the development of new statistical methodologies for analysing geophysical data. Such data include seismic and electromagnetic measurements, as well as newer data types such as fiber-optical sensing data. The project will also focus on using statistical methodologies for research developments in the centre. Core components of his PhD project include Bayesian hierarchical modeling and methods for conditioning in such models. He will aim for a coherent interpretable modeling approach honoring the large-size spatio-temporal data. Khanh will work on the statistical foundations for such frameworks.

 

Hybrid Machine Learning Approaches for Explainable Reservoir Inflow Forecasting

Hybrid Machine Learning Approaches for Explainable Reservoir Inflow Forecasting

PhD candidate: Rahmathulla Madathi Parambath
Supervisor: Jayaprakash Rajasekharan
Co-supervisor: Hossein Farahmand
Co-supervisor:

In Norway, hydropower is the primary source of electricity, and it accounts for 96% of electricity generation. Due to the long-term reservoir storage constraints in hydropower dominated systems, the resource scheduling should be done for a sufficiently long-time period and with an appropriate representation of uncertainties (such as inflow to reservoirs, wind power generation, demand etc.). Existing hydropower scheduling models face many shortcomings when dealing with such uncertainties. The IntHydro project aims to develop and demonstrate a new hydropower scheduling tool based on machine learning techniques, which manages the hydropower plants more efficiently and effectively through optimizing water resource management and multi-dispatch between hydropower and variable renewable energy sources. In this context, the main task of Rahmath in the doctoral work will be two-fold. First, to integrate machine learning techniques in different stages of hydropower scheduling models and define comprehensive coupling principles between the strategic and operational modelling. Second, to develop a prototype for fundamental hydropower scheduling that allows modelling of RES on a detailed time scale.

 

Natural Language Processing for Personalized Content Summarization

Natural Language Processing for Personalized Content Summarization

PhD candidate: Vandana Yadav
Supervisor: Jon Atle Gulla
Co-supervisor:
Co-supervisor:

Content summarization uses machine learning to extract the main ideas of individual texts or collections of text. It may also be used to explain recommendations and provide explanatory features in artificial intelligence in general. Central to the technique is the use of machine learning and language models to interpret text and contextualize content with respect to users or tasks at hand. Summarization is often combined with other NLP techniques to provide personalized summaries or build conversational systems that make use of external sources. Vandana will develop new summarization techniques that are incorporated into generative conversational systems and personalized with respect to users and contexts. The research will address both English and Scandinavian languages. The expected results include industrial prototypes in collaboration with industry partners as well as high-quality publications in top NLP venues.

 

Digital Twin

DIGITAL TWIN

A Digital Twin (DT) can be seen as a copy or models of a physical asset (from a single thermometer to a whole country, or even a human being), often connected through sensors and data. DT technology is envisioned to play a key role in the digital transformation of the society and many industry sectors. It can be an integrated part of other key technologies like IoT (data / state), AI (analytics / decision support), XR (visualization / dashboards) and Security (access / protect). DTs can be both static or more dynamic / real-time, and everything in between. A DT can be data-driven, physical/mathematical-driven or hybrid-driven. Its applications can range from planning, design, simulation, optimization and construction / manufacturing to monitoring, diagnostics, prevention/prediction, decision support, automation, maintenance, destruction/recycling and documentation. DTs can exist much longer than their physical counterparts (life-cycle property: from long before cradle to long after grave) and DTs can scale from a single device to complex systems (hierarchical property: e.g. sensor, room, floor, building, city, country). The industry is increasingly using DTs in their continuous strive to transform their business and many of the largest companies in Norway are among the key users of this technology. 

Digital Twins and Artificial Intelligence for Improved Personal Health and a more Sustainable Health Care System

Digital Twins and Artificial Intelligence for Improved Personal Health and a more Sustainable Health Care System

PhD candidate: Hamza H. Mohammed 
Supervisor: Frank Lindseth
Co-supervisor:  Trym Holter
Co-supervisor: Artur Serrano

 

We envision that a digital twin ecosystem (One Citizen - One Digital Twin) could substantially contribute to the effective realization of such a health care system. A Digital Twin (DT) is a digital/virtual representation/model of the physical twin/counterpart (PT), often connected through sensor data from the PT that enables the DT to provide feedback and predictions to the PT. DT technology is increasingly being used in domains like construction and manufacturing and is expected to play a key role in the digital transformation of many areas in the coming years. In the case of health-related digital twins (DTs) this means that your own personal twin would look after you throughout life, that this twin, that you are in charge of, will know as much as possible about you, and that the combined data from all the twins are used to generate new actionable knowledge applicable in various fields to both yourself (self-management of health) and the health care system in general (e.g. decision support) through the functionally provided by your twin (i.e. bridging the gap between citizens and the health-care system).

Hamza will contribute to the realization of a proof-of-concept DT ecosystem, focusing on wearables and state-of-the-art AI research connected to the analysis of vital signs and time series. He will also be given a unique opportunity to form how a future data-driven health care system might look like. The project is part of ongoing activity and related projects for health and medicine (e.g. wearables, distance follow-up, home hospital) among key NTNU faculties and departments with associated partners and collaborators.

 


Digital Twin for Autonomous Ships

Digital Twin for Autonomous Ships

PhD candidate: Daniel Menges  
Supervisor: Adil Rashid
Co-supervisor:  Edmund Førland Brekke
Co-supervisor: Anastasios Lekkas

Digital twinning is now an important and emerging trend in autonomous transport. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, it is bound to play a transformative role not only in how we design and operate cyber physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. At the heart of digital twins are real-time, accurate, generalizable, trustworthy and self-evolving models and hence the focus of the research is placed here. More specifically the research topic within the context will be to create better situational awareness for autonomous ships by instilling physical realism into their digital twins through: Developing tools and algorithms for better description and perception of the internal condition of the ship´s performance, condition of the various components of the ship etc.

Developing tools and algorithms for better description and perception of the external environmental environment of the ship: eg-. Weather condition, state of the ocean

Better comprehension of the immediate surrounding like how the other vessels, boats are communicating and maneuvering around.

Predicting the situation on a short and long term horizon for better reactive and planned navigation


Decision Support Systems for Autonomous Vessels

Decision Support Systems for Autonomous Vessels

PhD candidate: Luka Grgičević
Supervisor: Erlend Magnus Lervik Coates
Co-supervisor: Ottar Osen
Co-supervisor: Robin T. Bye
Co-supervisor: Thor I. Fossen

 

This position is a part of Cyber-Physical Systems Laboratory (CPS Lab) and NTNU SFI AutoShip. Luka is developing algorithms that are enabling Decision Support Systems integrated into Autonomous Navigation System on Maritime Autonomous Surface Ships. The thesis will challenge problems in collision risk assessment, path planning, weather routing, and complex multi-vessel situation resolution. The provided solutions might solve currently open issues and facilitate Digital Twin technologies in urban and car ferries.

 


Predictive digital twin for offshore wind farms

Predictive digital twin for offshore wind farms

PhD candidate: Evi Elisa Ambarita
Supervisor: Agus Hasan
Co-supervisor:  Anniken Susanne T. Karlsen
Co-supervisor: Francesco Scibilia
Co-supervisor: Frank Lindseth
Co-supervisor: Steffan Sørenes

 

The idea of this project is to leverage the use of AAS for digital twin development of offshore wind farm. Evi will investigate current applications of AAS in other industries that could be relevant for wind farms, possibility to transfer AAS learning and solutions from other industries. In general, this task should look at the opportunities of using the AAS framework in the offshore wind industry and highlight possible challenges for implementation, which include architecture design, data requirements, predictive analytics, information modeling, and simulation and visualization solutions for asset management and predictive maintenance. Furthermore, Evi will implement prototypes of simulation and visualization solutions based on Digital twin (AAS-based). Use cases will be provided from the Hywind Tampen wind farm operated by Equinor.

 

Autonomy and Simulation for a Sustainable and Carbon-Neutral Mobility Future

Autonomy and Simulation for a Sustainable and Carbon-Neutral Mobility Future

PhD candidate: Florian Wintel
Supervisor: Frank Lindseth
Co-supervisor:  Gabriel Kiss
Co-supervisor: Adil Rasheed

 

Florian's PhD project will investigate both modular (mapping and localization, perception and prediction, planning and control) and end-to-end (imitation and reinforcement learning) approaches to autonomous driving in a Nordic environment. Training and validation of autonomous agents (from shuttle busses to last-mile delivery robots) will be done in simulated environments (e.g. CARLA, NVIDIA DRIVE Sim) of a HD-map / Digital Twin representation of the area in question, as well as in the real-world environment using our in-house full-scale research platform for autonomous driving. Agents should learn to co-exist with humans in a real-world mobility setting. Current traffic patterns will be visualized and future “what-if” scenarios will be simulated (e.g. NVIDIA Omniverse) before physically constructing the optimal solution for a given area.

 

The effect of three-dimensional geographical characteristics on the demand dynamics of urban transportation modalities

The effect of three-dimensional geographical characteristics on the demand dynamics of urban transportation modalities

PhD candidate: Muhammad Tsaqif Wismadi
Supervisor: Yngve Karl Frøyen
Co-supervisor: Kelly Pitera
Co-supervisor: Adil Rasheed

 

Tsaqif's research is part of a larger interdisciplinary project and lab, Mobility Elgeseter which aims to explore innovative and sustainable mobility solutions in an urban, campus environment. Transport models are used to replicate various aspects of the transport system, and to predict and evaluate the results of changes to the system. Transport models are used for analyzing strategic decisions about the transport system, either to achieve goals or to estimate impacts of suggested initiatives. The objective of the PhD projects is to improve the state of the art of urban transport models by capitalizing on increased digitization and big data. The research will explore how sources of dynamic data and passive data can be utilized to further develop and improve transport modelling. In addition to addressing increased digitization, the projects will aim to further developed urban transport models to address the shifting focus from car passenger transport to active transport and public, and the need to integrate freight transport with passenger transport within models. In addition to the theoretical research within the projects, there will also be a strong focus on the applied aspects of this research. This includes the development of transport models for the Elgeseter case study area which can be used to understand both the current mobility picture and future mobility scenarios. Finally, the projects will consider the integration of such models into an interactive platform for modeling and visualization in the 3D virtual environment of a digital twin, and consider how model inputs, results, and uncertainties can be better communicated to decision-makers.

 

Measureing impact of sustainable transport initiatives on interactions of multimodal road users: Simulation-based modelling with empirical data considering digital twins

Measureing impact of sustainable transport initiatives on interactions of multimodal road users: Simulation-based modelling with empirical data considering digital twins

PhD candidate: Zakiya Aryana Pramestri
Supervisor: Trude Tørset
Co-supervisor: Kelly Pitera
Co-supervisor: Yngve Karl Frøyen
Co-supervisor: Kathrine Strømmen

 

Zakiya's research is part of a larger interdisciplinary project and lab, Mobility Elgeseter which aims to explore innovative and sustainable mobility solutions in an urban, campus environment. Transport models are used to replicate various aspects of the transport system, and to predict and evaluate the results of changes to the system. Transport models are used for analyzing strategic decisions about the transport system, either to achieve goals or to estimate impacts of suggested initiatives. The objective of the PhD projects is to improve the state of the art of urban transport models by capitalizing on increased digitization and big data. The research will explore how sources of dynamic data and passive data can be utilized to further develop and improve transport modelling. In addition to addressing increased digitization, the projects will aim to further developed urban transport models to address the shifting focus from car passenger transport to active transport and public, and the need to integrate freight transport with passenger transport within models. In addition to the theoretical research within the projects, there will also be a strong focus on the applied aspects of this research. This includes the development of transport models for the Elgeseter case study area which can be used to understand both the current mobility picture and future mobility scenarios. Finally, the projects will consider the integration of such models into an interactive platform for modeling and visualization in the 3D virtual environment of a digital twin, and consider how model inputs, results, and uncertainties can be better communicated to decision-makers.

 

Automated AI/Computer Vision Driven Generation of Digital Twins for Sustainable Mobility Infrastructure

Automated AI/Computer Vision Driven Generation of Digital Twins for Sustainable Mobility Infrastructure

PhD candidate: Sachin Verma
Supervisor: Gabriel Kiss
Co-supervisor:
Co-supervisor:

 

The main aim of Sachin's project is to build a static digital twin of the MobilityLab’s focus area that will act as a baseline for all further simulations and will be extended to be able to receive dynamic information from IoT sensors. An accurate digital representation of the focus area will be created, based on existing geometric information and high-resolution aerial photographs. Raw data is already available for the project either as public or previously acquired datasets, additionally during the project more raw-data will be acquired with an in-house full scale research platforms (Kia e-Niro with an NVIDIA DriveWorks stack as well as dedicated high-resolution surveying drones). The final aim of his project is full automation of the conversion process from raw data to a usable digital twin representation (i.e. AI based methods and handling of Big Data arriving in real-time from the research platforms). Furthermore, improving the model via automatic texturing and interactive editing will also be investigated (e.g. Instant NeRF or Block NeRF). The intended platform for the development of the digital twin will be NVIDIA Omniverse. models into an interactive platform for modeling and visualization in the 3D virtual environment of a digital twin, and consider how model inputs, results, and uncertainties can be better communicated to decision-makers.

 

Modeling of Integrated multimodal urban mobility: Integrating mass transit with on demand mobility options

Modeling of Integrated multimodal urban mobility: Integrating mass transit with on demand mobility options

PhD candidate: Zelalem Birhanu Biramo
Supervisor: Trude Tørset
Co-supervisor: Kelly Ann Pitera
Co-supervisor:

 

Zelalem's research is part of a larger interdisciplinary project and lab, Mobility Elgeseter which aims to explore innovative and sustainable mobility solutions in an urban, campus environment. Transport models are used to replicate various aspects of the transport system, and to predict and evaluate the results of changes to the system. Transport models are used for analyzing strategic decisions about the transport system, either to achieve goals or to estimate impacts of suggested initiatives. The objective of the PhD projects is to improve the state of the art of urban transport models by capitalizing on increased digitization and big data. The research will explore how sources of dynamic data and passive data can be utilized to further develop and improve transport modelling. In addition to addressing increased digitization, the projects will aim to further developed urban transport models to address the shifting focus from car passenger transport to active transport and public, and the need to integrate freight transport with passenger transport within models. In addition to the theoretical research within the projects, there will also be a strong focus on the applied aspects of this research. This includes the development of transport models for the Elgeseter case study area which can be used to understand both the current mobility picture and future mobility scenarios. Finally, the projects will consider the integration of such models into an interactive platform for modeling and visualization in the 3D virtual environment of a digital twin, and consider how model inputs, results, and uncertainties can be better communicated to decision-makers.

 

Area of Radio Channel Measurements and Modeling in Maritime Scenarios

Area of Radio Channel Measurements and Modeling in Maritime Scenarios

PhD candidate: Giacomo Melloni
Supervisor: Torbjörn Ekman
Co-supervisor:
Co-supervisor:

 

Giacomo will work on challenging research problems related to radio channel measurements and modeling, within the general area of radio-system design. The activities include Massive MIMO radio channel measurements in the Trondheim fjord, accompanied by channel modeling. With insights gained from measurements, the main objective of the research is to identify current solutions' weaknesses and suggest improvements, design concepts, and deployment strategies for large antenna maritime communication systems, in collaboration with SINTEF Digital and Telia. As part of the research activities, he will collaborate actively with some of the partners. Also, he will be affiliated with the IoT@NTNU and with the Norwegian Open AI lab, and will be encouraged to have a research stay with one of the collaborating universities outside Norway during the research period.

 

Goods transport including applications of dynamic/big data

Goods transport including applications of dynamic/big data

PhD candidate: Irene Hofmann
Supervisor: Trude Tørset
Co-supervisor:
Co-supervisor:

 

Irene's positions are part of a larger interdisciplinary project and lab, Mobility Elgeseter (link) which aims to explore innovative and sustainable mobility solutions in an urban, campus environment. Transport models are used to replicate various aspects of the transport system, and to predict and evaluate the results of changes to the system. Transport models are used for analyzing strategic decisions about the transport system, either to achieve goals or to estimate impacts of suggested initiatives. The objective of the Irene's project is to improve the state of the art of urban transport models by capitalizing on increased digitization and big data. The project will explore how sources of dynamic data and passive data can be utilized to further develop and improve transport modelling. In addition to addressing increased digitization, the project will aim to further developed urban transport models to address the shifting focus from car passenger transport to active transport and public, and the need to integrate freight transport with passenger transport within models. In addition to the theoretical research within the projects, there will also be a strong focus on the applied aspects of this research. This includes the development of transport models for the Elgeseter case study area which can be used to understand both the current mobility picture and future mobility scenarios. Finally, the project will consider the integration of such models into an interactive platform for modeling and visualization in the 3D virtual environment of a digital twin, and consider how model inputs, results, and uncertainties can be better communicated to decision-makers.

 

Machine Learning for Maritime Communication System Performance Prediction

Machine Learning for Maritime Communication System Performance Prediction

PhD candidate: Manju James
Supervisor: Kimmo Juhani Kansanen
Co-supervisor:
Co-supervisor:

 

The radio propagation channel behavior, including its statistical variation in time and space, depends on the location of transmitter and receiver, leading to location-dependent achievable data rates and reliability. The maritime channel, in addition, will depend on weather conditions that may change the propagation environment (sea) geometry. Manju's work studies the use of data-driven, machine learning trained spatial models that can be used to predict the achievable rates and reliabilities of the maritime channel, and the related radio system. The data sources foreseen are the radio system channel quality indicators at the physical layer, the location information, and eventually other data sources that can be used to improve the prediction accuracy (e.g. weather information).

 

Internet of Things

INTERNET OG THINGS

This thematic area aims at advancing knowledge and understanding of IoT technology, which is a key enabler for the digital transformation of the society and is used in a large variety of domains (moving out of traditional ICT and ranging from e-health to Industry 4.0). IoT technology embraces heterogeneous networks of devices with various combinations of interaction in terms of sensing, communication, computation, and control, thus a multi-disciplinary approach is essential in order to exploit and manage its full potential. There will be 7 linked PhD positions announced focusing mostly on Energy, Mobility, and Ocean, with a common theoretical framework relying on effective processing of raw sensor data.  


Design Methodology for Energy-Harvesting-based IoT Systems

Design Methodology for Energy-Harvesting-based IoT Systems

PhD candidate: Lukas Liedtke
Supervisor: Magnus Jahre
Co-supervisor:  Frank Alexander Kraemer
Co-supervisor:  Per Gunnar Kjeldsberg

Experimental Platform for Intermittent IoT Research (EPIoT) which will enable research on intermittent IoT applications by designing the platform around the energy and sensor subsystems. More specifically, EPIoT will combine a range of energy harvesters (see [8]) with configurable energy storage (e.g., super-capacitor banks) and slow (e.g., temperature and humidity) and fast sensors (e.g., vibration and sound). EPIoT’s compute and communication System on Chip (SoC) will inspect the energy storage to dynamically selecting operation points in energy-equilibrium. Once operational, EPIoT can serve as an enabler of further research. For instance, multi-node systems can be straightforwardly implemented as EPIoT nodes are scalable by design (i.e., they are self-powered and communicate wirelessly). As aforementioned, intermittent computing systems are critical to deliver the scalability required by foreseen IoT applications, and EPIoT will enable better understanding the fundamental energy versus performance trade-offs facing such applications. Moreover, EPIoT will be directly applicable to emerging applications such as predictive maintenance (highly relevant to the manufacturing and energy domains) as well as (remote) health monitoring. Moreover, removing batteries from ULP-nodes will contribute to making IoT applications more sustainable (by reducing waste).

 


Ship-Shore Radar Network

Ship-Shore Radar Network

PhD candidate: Lukas Herrmann
Supervisor: Egil Eide
Co-supervisor:  Edmund Førland Brekke
Co-supervisor:  Andreas Brandsæter

The key ambition of the project will be to develop an advanced real-time network of shore based and ship-based maritime radars to assist monitoring and guidance of autonomous ships. The Trondheim fjord was appointed as the world’s first accredited test area for autonomous ships in 1996. As a part of building this research facility, NTNU and SINTEF will establish a network of shore-based maritime radars to be applied both for monitoring and guidance of autonomous vessels and for research application within the fields of radar scattering of sea surface, radar detection and tracking, and guidance/control. The radars will be connected in a real-time network using 5G or similar broadband radio systems. In addition, the network architecture will allow for real-time interchange of radar data between ships and ship-shore. All data will be jointly processed and stored in a shore-based server. Resulting target tracks will be distributed to all ships in the area to enhance the situation awareness for both manned and unmanned vessels.

The research and project work will put emphasis on developing efficient signal processing and real-time communication architecture for exchange of data between radar sites and users. Activities and goals under the PhD-work will be:
• System studies and system architecture of the radar data processing chain and radar data distribution system with low latency.
• Developing advanced algorithms for radar detection, sea and rain clutter filtering, and target tracking based on low level data fusion from a multitude of ship-borne and shore-based radars.
• Design and specifications of the access and network protocols, in terms of satellite link characterization for availability, capacity and reliability.
• Experimental testing and verification of the network with real vessels in the fjord. The goal will be to demonstrate the applicability of a complete radar network both for maritime operations and for general research work.

 

Ultra-Low Power Integrated Circuit Design for Self-powered Internet of Things

Ultra-Low Power Integrated Circuit Design for Self-powered Internet of Things

PhD candidate: Muhammad Umer Khalid
Supervisor: Snorre Aunet
Co-supervisor:  Trond Ytterdal
Co-supervisor:  Magnus Själander
Co-supervisor:  Rakesh Kumar

We need ultra-low power IoT devices that can harvest their energy from their surroundings and operate without a battery to accelerate large-scale deployment of IoT devices and the advent of a sustainable smart world (smart health, cities, transportation, etc.). Subthreshold circuits have been shown to achieve power and energy savings of up to orders of magnitude as compared to conventional of the shelf components, but extreme vulnerabilities to process-, voltage-, and temperature variations on chip still hinder widespread adoption in industry. The low supply volage at which subthreshold logic operates results in low switching speeds, which sometimes cause limitations. It is therefore foreseen that IoT devices require both subthreshold as well as conventional logic to meet both their energy and performance requirements. The objective of this PhD project is to investigate design principles for ULP devices. Umer will investigate Ultra-Low Power Internet of Things (ULP-IoT) devices that can collect sensor input and perform computational tasks, monitoring energy levels and waking up additional parts of the system when, e.g., more performance or wireless communication is required, and enough energy is available to the system.

 

Acoustics of fiber optic cable sensing for monitoring

Acoustics of fiber optic cable sensing for monitoring

PhD candidate: Josephine Nell Schulze
Supervisor: John Robert Potter
Co-supervisor:
Co-supervisor:

 

Josephine will explore novel applications of fibre optic cable sensing for environmental monitoring. This includes using DAS for passive detection of ships and marine mammals for deconfliction using AIS, and perhaps also exploring the potential for AUVs to communicate over long distances while submerged using DAS as a one-way acoustic channel to shore.

 

Edge Intelligence for Dynamic Digital Twins

Edge Intelligence for Dynamic Digital Twins

PhD candidate: Omkar Bhoite
Supervisor: Kimmo Juhani Kansanen
Co-supervisor:
Co-supervisor:

 

A digital twin is defined as a virtual representation of a physical asset or process enabled through data and simulations for real-time prediction, optimization, monitoring, control, and improved decision making. For real-time operation, the twin requires continuous updating with new measurements from its observation platforms. However, different sensors produce significantly different amounts of data, with different time-criticality and importance for the twin. Omkar willT focus on the coupling between the twin and the different kinds of sensors, and will work with pre-processing, compression, optimization and communication strategies to support accurate and timely twin operation while transmitting least data possible. Machine learning and AI-based strategies that learn and adapt to the characteristics of the sensors, and the requirements of the twin will be of special interest.

 

Information and Cyber Security

INFORMATION AND CYBER SECURITY

The increasing digitalization of society comes along with more vulnerabilities of and increased dependency on digital infrastructure and systems. Successful digital transformation cannot be achieved without ensuring that digital services will function as expected, anywhere, and at all times, whilst preserving the security of information and the privacy of individuals. Information security refers to the protection of digital systems and infrastructure, the data on them, and the services they provide, from intentional or accidental harm. Information security is an interdisciplinary area that combines information and communication technologies, management, law, economics, mathematics, and psychology. It is envisaged to announce 8 PhD positions and it is expected that the PhD students that will be recruited will acquire knowledge and will develop skills that will facilitate the secure digital transformation and will contribute to creation of significant positive impact including through reduced negative impact (due to reduced exposure to cyber risks) that ultimately leads to sustainable value creation.  


Dynamical Modeling of Sustainable Digital Markets

Dynamical Modeling of Sustainable Digital Markets

PhD candidate: Gabriel Andy Szalkowski
Supervisor: Iwona Maria Windekilde
Co-supervisor:  Harald Øverby
Co-supervisor:  Patrick Mikalef

Digital markets are dominated by a few global digital conglomerates, including Apple, Amazon, Facebook, and Google. These companies have huge impact on the innovation, production, and trade of digital services. There is a concern that these markets are not performing optimally, witnessed by recent legal actions from both the EU and the US Competition Authorities (see e.g., US v. Google (2020), US v. Facebook (2020), and EU v. Apple (2021)). A deeper understanding of these digital markets and their business models is needed to (i) restore competition, (ii) promote innovation, and (iii) ensure sustainability according to SDG 9.

The main research goal for Gabriel is to (i) analyze current digital markets and business models using system dynamic modelling and to (ii) develop and test new policies and business models promoting competition and innovation in the context of sustainability in future digital markets. His PhD position is highly cross-disciplinary, combining knowledge from technology, economics, business, and law.

 


Lattice-based cryptography and homomorphic computation

Lattice-based cryptography and homomorphic computation

PhD candidate: Cristian Alonso Baeza Miranda
Supervisor: Anamaria Costache
Co-supervisor:  Danilo Gligoroski
Co-supervisor:  Tjerand Silde
Co-supervisor:  Jiaxin Pan

Cristian will work on cryptographic algorithms and protocols in the general areas of privacy-preserving computation, post-quantum cryptography or lightweight cryptography.

 

Digital Twin Security Models and Mechanisms

Digital Twin Security Models and Mechanisms

PhD candidate: Jessica Barbosa Heluany
Supervisor: Vasileios Gkioulos
Co-supervisor:  Sokratis Katsikas
Co-supervisor:  Siv Hilde Houmb

Digital twins have evolved from passive monitoring and state estimation systems to integrated sociotechnical mechanisms that are essential for strategic modeling and planning, as well as operational real-time monitoring and control of cyber-physical systems. In order to support these functionalities, the operation of digital twins relies heavily on maintaining fidelity and synchronization with the production systems to which they are targeted. This is particularly important in safety-critical systems, from power station generators to manufacturing systems or production facilities in the oil and gas industry.

 

Secure infrastructure for cyber-physical ranges

Secure infrastructure for cyber-physical ranges

PhD candidate: Vyron Kampourakis
Supervisor: Vasileios Gkioulos
Co-supervisor: Sokratis Katsikas
Co-supervisor: Habtamu Abie

Cyber Physical Systems constitute the core of Critical Infrastructures, yet their architectural and operational characteristics are not thoroughly captured by contemporary cyber ranges, which are commonly narrow in scope or purposefully aligned with subsections of specific target systems. The anticipated use of the physical reference environments investigated, modelled, and integrated in this work will be twofold, namely as demonstrators for education and dissemination, but also as testbeds for activities related to research and training.

 

Dynamic Interdependency Models for Critical Infrastructures

Dynamic Interdependency Models for Critical Infrastructures

PhD candidate: Yana Bilous
Supervisor: Stephen Dirk Bjørn Wolthusen
Co-supervisor: Vasileios Gkioulos
Co-supervisor: Nils Kalstad

Graph and combinatorial models representing dependencies and interdependencies and flows of resources have been studied intensively also with a view to representing susceptibility of represented structures to attacks. However, we are interested to study highly sparse dynamic graphs and structures within these as well as efficient algorithms that allow the determination particularly of local properties. This is motivated by the application domains of modern infrastructure networks whose topology will usually be dynamic and may only be partially known; this would render insights into properties that would e.g. indicate vulnerabilities obsolete or wrong. The ability to obtain timely insights also for large networks would hence allow targeted responses and resilience mechanisms that are widely applicable.

 

Digital Twin and AI/ML Supported Threat Modeling Framework for Cyber-Attack Prediction and Projection

Digital Twin and AI/ML Supported Threat Modeling Framework for Cyber-Attack Prediction and Projection

PhD candidate: Gizem Erceylan
Supervisor: Vasileios Gkioulos
Co-supervisor: Sokratis Katsikas
Co-supervisor: Sandeep Pirbhulal

Digital twins have evolved from passive monitoring and state estimation systems to integrated sociotechnical mechanisms that are essential for strategic modeling and planning, as well as operational real-time monitoring and control of cyber-physical systems. In order to support these functionalities, the operation of digital twins relies heavily on maintaining fidelity and synchronization with the production systems to which they are targeted. This is particularly important in safety-critical systems, from power station generators to manufacturing systems or production facilities in the oil and gas industry.

 

Secure, Human-Centered XR Experiences in Critical Sectors

Secure, Human-Centered XR Experiences in Critical Sectors

PhD candidate: Camille Rose Sivelle
Supervisor: Katrien De Moor
Co-supervisor: Bian Yang
Co-supervisor: David Palma

Efforts to strengthen cybersecurity and resilience of critical sectors such as healthcare, smart districts, manufacturing,… go hand in hand with technological innovations and novel approaches and measures. For instance, the advanced use of AI and big data analytics leads to increased intelligence capabilities (e.g., for video-based or digital traces-based surveillance, remote monitoring). In addition, technologies enabling remote presence like Extended Reality (XR) are seen as a key enabler allowing to reduce physical human involvement, for instance in dangerous or life-threatening environments. XR technologies also pave the way for novel, immersive experiences in critical sectors (e.g., remote caregiving, …). While these innovations and technologies create new opportunities and enable a range of cybersecurity measures, they also often have an invisible flip side: their ethical implications (e.g., related to trust, transparency, control, privacy, digital inequality, bias, …) are to date still under-adressed.

 

Extended Reality

EXTENDED REALITY

Extended Reality gathers together the fields of Virtual Reality, Augmented Reality and Mixed Reality in a holistic way aiming at providing interactive digital content immersing the user completely and providing the best possibly Quality of Experience to the user. Extended Reality is by nature transdisciplinary and requires both soft and hard skills in the intersection between art and technology.  


The applications of Microscopic Virtual reality traffic simulations as risk assessment tool of future mobility concepts

The applications of Microscopic Virtual reality traffic simulations as risk assessment tool of future mobility concepts

PhD candidate: Baher Gunied
Supervisor: Andrew Perkis
Co-supervisor:  Gabriel Kiss
Co-supervisor: Trude Tørset
Co-supervisor: Eirin Olaussen Ryeng
Co-supervisor: Ekaterina Prasolova-Førland

 

For the XR experience Baher will be researching on the use of Interactive Digital Narratives (IDNs) as a framework to build and assess the experiences. Interactive digital narratives (IDN) is an expressive narrative form in digital media implemented as a computational system and experienced though a participatory process. Currently the application of interactive digital narratives (IDN) has various forms (Narrative-focused games, interactive documentaries, journalistic interactives, installation pieces, XR experiences, narrative interfaces to big data, etc). IDN allows its audiences to experience the consequences of a series of choices and reconsider these choices through replay as well as ability to record change. In addition, IDN can contain multiple competing perspectives in a single work and enable its audiences to experience them within a single comprehensive space. With a focus on participatory forms the PhD will contribute to the MobilityLab Elgeseter project. Baher's project is part of a new large interdisciplinary initiative called Mobility Lab Elgeseter. The center is divided into the three focus areas 1) stakeholder needs for good mobility, 2) mobility as a system / transportation models, and 3) digital technologies for green mobility, that will work closely together to realize innovative and sustainable future mobility solutions in the urban environment. Within area 3) various enabling technologies will be used to automate the process of building and using digital mobility infrastructure twins (i.e. holistic/unified, life cycle, hierarchical, integrated, dynamic/updated representations of the physical road network) for collaboration, simulation, carbon/energy footprint calculations, road condition monitoring, predictive maintenance, automated traffic management and other forms for value creation (general knowledge will be developed that can be scaled up and used elsewhere).

 

Extended Reality and Digital Twins Based on Computer Vision and AI

Extended Reality and Digital Twins Based on Computer Vision and AI

PhD candidate: Sabine Fischer
Supervisor: Gabriel Hanssen Kiss
Co-supervisor:
Co-supervisor:

 

Sabine will work on challenging research problems related to radio channel measurements and modeling, within the general area of radio-system design. The activities include Massive MIMO radio channel measurements in the Trondheim fjord, accompanied by channel modeling. Her position is part of SFI AutoShip, the new 8-year research-based innovation Center.

 

EU project

EU project

EU logo This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034240.