Background and summary of fellowship
Over the last decade, academia and the industry of networked systems have become more and more interested in novel real-time applications. These applications arise for one in the area of Cyber-Physical Systems (CPS) where essentially time-sensitive processes are to be governed by direct actuation. On the other hand, these applications also arise in the context of providing automated feedback to human users, for instance, in augmented reality as well as cognitive assistance. Such interactive applications are very powerful with respect to their future implications for professional education, ambient intelligence as well as leisure. It is therefore likely that they will have a profound impact on networked systems. Nevertheless, from a fundamental perspective, we have very little understanding of the efficient operations of networked systems for such interactive applications today.

The goal of this project is to provide fundamental performance models for these interactive applications and the operation of underlying networked systems. In contrast to state-of-the-art, our key approach is to capture the essential trade-offs through a novel notion of utility of the received information over time, and subsequently to strive for system optimizations. Central to our application are novel sampling policies, which we derive by leveraging Markov Decision Processes. By this, we aim at providing a cornerstone for the design of future networked systems exposed to interactive applications.

Background and summary of fellowship
Sarunas Girdzijauskas’ research interests are on the intersection of distributed systems and machine learning fields and fall under “Cooperate” and “Learn” research themes, addressing “Smart Society” as well as “Rich and Healthy Life” societal contexts of Digital Futures Strategic Research Programme.

There are many societal problems plaguing current AI services provided by modern Big Tech behemoths, which collect and process user data in a centralized manner. Such data collection and processing inevitably leads to a wide spectrum of issues from data privacy, system security to severe scalability and power consumption issues. Sarunas Girdzijauskas’ research focuses on solutions enabling the transition from classical centralized machine learning to Federated and Decentralized Machine Learning technologies. A particular focus is on developing decentralized architectures for graph analytics and graph machine learning which would enable a wide range of current AI services (e.g., product recommendation systems, social network news feeds etc.) to be provided without the need of centrally collecting data.

Background and summary of fellowship
As data generation increasingly takes place on wireless IoT devices, Artificial Intelligence and Machine Learning (AI/ML) over the Internet of Things (IoT) wireless networks becomes critical. Many studies have shown that state-of-the-art wireless protocols are highly inefficient or unsustainable to support AI/ML services. There is a consensus in the forefront research communities that AI/ML for the connected world is at its infancy and much will have to be investigated in the next decade. In this research project, I will follow a research plan dived into roughly three open research sub-directions:

  1. Theoretical foundations of distributed AI/ML: I will contribute to making AI/ML theory aware of the characteristics of the wireless networks, and will fundamentally rethink it.
  2. Theoretical foundations of AI/ML to design wireless networks: I will contribute to radically redesign by AI/ML the future communication protocols for critical societal applications, due to the deficiencies of model-based methods. This includes also the optimisation of the current wireless protocols using AI/ML, which is at the very beginning.
  3. Theoretical foundations to redesign wireless for supporting AI/ML services: future wireless networks will have to support pervasive AI/ML services. Current communication protocols are highly insufficient for such purposes. I will contribute to establishing fundamentally new wireless protocols and theories, such as “over the air function computations”, to support AI/ML services over IoT.

About the project

Objective
We propose to build a physical prototype of a modular lower-limb exoskeleton system with a digital interface to a real-time variable controller. The prototype will be equipped with motors that provide variable control to different joints and joint ranges of motion while supporting real-time control of its kinetic properties. By varying the assistive strategies in the exoskeleton system via a digital interface, we will enable human-in-the-loop (HILO) optimization to find optimal control strategies for different users and goals.

Background
Persons with physical disabilities are the largest minority group in the world, and global trends in ageing populations indicate an expected increase in the population affected by disability. In Sweden, musculoskeletal disorders are one of the most common causes of long-term disability.

Wearable robotic assistive exoskeletons have rapidly developed in the past decades, yet only a handful of products are used frequently, either within or outside research environments. A major reason is that a device must be adjusted for user compliance and efficacy for optimal effect and comfort. Using methods for automatically discovering, customizing, and continuously adapting assistance could overcome these challenges, allowing exoskeletons and prostheses to achieve their potential.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Engineering Science, Department of Engineering Mechanics and KTH School of Industrial engineering and management, department of Machine Design.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
The objective of the Improving air quality and Health risk forecasts by data-driven modelling of traffic and atmospheric environment (iHorse) project is to increase the accuracy of air pollution and health risk forecasts. The current system relies on deterministic meteorological dispersion modelling to forecast the impacts of emissions on concentrations. One of the main uncertainties is forecasting emissions from road traffic which is a dominant source of air pollution in the urban environment. In this project, emissions are calculated based on detailed information on the vehicle fleet composition and emission factors. In addition, a novel, innovative data-driven deep learning model will be developed and integrated with the air pollution and traffic modelling processes. The aim is to improve the forecast of air pollutants, pollen and AQHI for the Greater Stockholm area.

Background
Air pollution is one of the leading causes of mortality worldwide. Acute effects of air pollution are due to short-term exposures that can lead to reduced lung function, respiratory infections and aggravated asthma. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. Predicting traffic emissions and induced air pollution has been an activity of high priority in the agenda of transport authorities and municipalities The project will also contribute to the use of artificial intelligence, identified as a key priority of the city of Stockholm and the SMart URBan Solutions (SMURBS; http://smurbs.eu/), an EU project involving several European cities with the overall aim to provide solutions based on Earth Observations that make cities more smart and sustainable.

Crossdisciplinary collaboration
The researchers in the team represent the Department of Environmental Science, SU and the School of industrial engineering and management, KTH.

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

To be announced

Results

To be announced

Publications

We like to inspire and share interesting knowledge!

Preprint is currently under review for the journal Atmospheric Chemistry and Physics (ACP):

  1. Department of Environmental Science, Stockholm University, Stockholm, Sweden
  2. Environment and health administration, SLB-analys, Stockholm, Sweden
  3. KTH Royal Institute of Technology, Dept. of Civil and Architectural Engineering, Stockholm, Sweden
  4. Department of Epidemiology, Lazio Region Health Service, Rome, Italy

DOWNLOAD APP! Link to the mobile phone App with air quality and health risk forecasts for Stockholm (in Swedish only).

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Photo: Johan Pontén

About the project

Objective
The Demonstrating Rich and Batteryless Human-Powered Interaction using Backscatter Communication (HumanScatter) project focuses on designing and prototyping interactive systems that are limited in energy usage to our bodily capabilities – technology driven by human power. No battery-free Bluetooth transceivers can directly receive data from unmodified Bluetooth devices such as smartphones. This project will investigate this challenge in combination with human-powered interactions with the ambition to create sustainable, robust, and resilient low-power technology and communication mechanisms. The goal is to achieve rich, battery-free, human-powered interactions using backscatter communication. The project will result in a new Bluetooth backscatter transceiver, multiple novels and intriguing demonstrators of human-powered interaction using backscatter techniques, and a final refined, fully working demonstrator suitable for long-term exhibition at the digital futures demo space.

Background
Throughout history, people have mostly relied on their bodily capacity, leading a human-powered or self-powered lifestyle. In contrast, today’s energy abundance has led to a replacement of human power by invisible centralized electricity production, enabling people to easily use magnitudes more energy than provided by the bodily limits of the individual. Backscatter communications and associated techniques offer a promising alternative to traditional communication technologies by lowering their power consumption so that they can often be powered only by radio waves or other energy-harvesting methods. While backscatter Bluetooth transmissions have been widely demonstrated, battery-free reception of Bluetooth packets remains challenging.

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH and the Department of Computer and Connected Intelligence Unit, RISE.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
This project aims to develop hardware and software for an open-source DNA test to enable point-of-care self-testing. This system will consist of a disposable and low-cost test strip, a low cost, and a small electronic device that carries out the tests. It will read the results digitally and communicate them directly to smartphones. The project will be carried out in a collaboration between a research lab at CBH School, the KTH prototype centre and hospitals for patient samples and testing. In the short term, this project can immediately be used for digital DNA detection of the novel SARSCoV-2 virus. More importantly, open-source, fully digital DNA tests can have a long-term effect on global health care by digitalizing and democratizing molecular diagnostics.

Background
The classical Nucleic-acid amplification testing (NAAT) requires several steps, and the current tests are mainly performed by highly trained personnel using advanced machinery in centralized laboratories. In the past decade, the Hamedi Lab and numerous others pioneered the development of analytical devices fabricated by using high throughput printing techniques on paper and other substrates to build diagnostic devices which are very cheap and simple to manufacture. All steps of liquid handling, storage of reagents, biochemistry, and electroanalytical detection, can be performed inside this paper. A few examples have shown that NAAT tests can be performed using these printed diagnostics devices.

Crossdisciplinary collaboration
The researchers in the team represent the School of Engineering Sciences in Chemistry, Biotechnology & Health, KTH and the School of industrial engineering and management, RISE.