About the project

Objective
This demo project will develop a prototype for mid-sized haptic seated interactions in collaboration with Volvo Cars. The demo will exemplify how to prepare drivers to dis -and re-engage with self-driving cars through the seat, create posture awareness and increase comfort for drivers. This is an urgent need for our collaborators at Volvo Cars, which impacts the safety and feasibility of semi- and automated cars on the roads. The prototype will be designed using novel haptic technologies and interaction design techniques that develop and strengthen a mutual collaboration between the user and the system. The project will result in technical innovation in mid-level haptics and the construction of design knowledge for an orchestrated meaningful touch of the body.

Background
Semi-autonomous cars are a growth area. Designing interactions within self-driving cars that are able to re-engage drivers in a driving issue in a timely and safe manner continues to be an open question for manufacturers and researchers. This project will demonstrate the feasibility and efficacy of using mid-sized haptics embedded within a car seat to re-engage and disengage a driver in a semi-autonomous car setting.

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, the School of Industrial Engineering and Management at KTH and RISE.

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

About the project

Objective
This project aims to support planning for universal access to clean cooking and contribute to Sustainable Development Goal 7 (SDG7) on Affordable and Clean Energy by radically integrating artificial intelligence (AI) methods into the open-source geospatial clean cooking model OnStove. To enhance OnStove, the project proposes implementing geospatial AI learning models to improve the spatial understanding of current technologies used for cooking and generate quasi-optimal transitions over an extended modelling period, considering as well behavioural issues. To achieve this, open-access geospatial information is combined with ground information gathered through available survey data. Finally, all comes together as a new open-source, user-friendly interface that will enable a broader audience to use the tool and ensure a long-term community of practice around clean cooking access modelling.

Background
Approximately 2.3 billion people still lack access to clean cooking worldwide, making them rely on traditional and polluting fuels. This practice poses challenges as households spend significant time collecting and cooking with traditional fuels, impacting women and children. Cooking with polluting fuels causes severe health consequences, estimated at around 3.2 million premature deaths annually from respiratory diseases. Moreover, traditional cooking exacerbates climate change and causes deforestation around the globe. On previous efforts, the first open-source geospatial tool for assessing and comparing the net benefits of various cooking solutions, OnStove, was developed. The tool was applied in the first study for sub-Saharan African countries, using Geospatial Information Systems (GIS) to evaluate cleaner cooking solutions in each square kilometre of the region. The tool considers benefits such as reduced morbidity, mortality, greenhouse gas (GHG) emissions, and time saved while factoring in investment costs, fuel purchase, and operation and maintenance.

By calculating the relative differences between current fuel stoves used and cleaner alternatives, OnStove determines a transition’s net benefit (benefits minus costs) and selects the options providing the highest net benefit in each square kilometre. Decision-makers can use the outputs to understand the impacts of achieving clean cooking access, including estimates on deaths avoided, time saved, GHG emissions avoided, health and GHG emissions costs avoided, total costs, and affordability constraints. OnStove facilitates decision-makers in identifying areas for action, guiding market strategies, and prioritizing investments to promote diverse clean-stove options in low- and middle-income countries. The tool has gained policy community interest, aiding energy access planning in Nepal and Kenya. It is also integrated into global initiatives like the World Resources Institute’s Energy Access Explorer and the International Energy Agency’s Clean Cooking Outlook Special Report.

Crossdisciplinary collaboration
Our project benefits from the expertise of a diverse team from the Department of Energy Technology and the Department of Sustainable Development, Environmental Science and Engineering at KTH Royal Institute of Technology. Furthermore, we collaborate closely with international organizations active in the clean cooking domain, such as the Clean Cooking Alliance (CCA), the World Resources Institute, The International Energy Agency, and Sustainable Energy for All. This expert group ensures that we have the technical and practical background to develop an innovative solution that addresses the project’s challenges and supports achieving SDG7’s clean cooking goals.

About the project

“Advancing real-time exoskeleton control for human-in-the-loop optimization” is a continuation of the completed project Real-time exoskeleton control for human-in-the-loop optimization.

Objective
Our proposal is 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 being capable of 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 in order to find optimal control strategies for different users and different 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 undergone rapid developments in the past decades, yet only a handful of products are used frequently, either within or outside of research environments. A major reason for this 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 Digitalize in Stockholm 2022 event:

About the project

Objective
The aim of the research team is to realise and research the first objects of programmable, robotic matter. Robotic matter consists of thousands or millions micro-scale components and forms objects that can autonomously change their shape and material properties. The research results can be the foundation for future generations of robotic matters configured with various physical functionalities.

Background
Robotic matter does not exist today, but if we can create and use it, we could solve several major societal challenges. The ultimate scenario is the opportunity to create all kinds of objects. An everyday and relevant example is using robotic matter to create and re-create packaging instead of using non-circular plastic materials. Another example is a decreased demand for transportation of goods when we can create any object we want in the location where it is needed.

Cross-disciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH) and the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH, KTH).

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

Press & Media
Article in Elektroniktidningen: https://issuu.com/etndigi/docs/etn2102ld
or download a pdf here: Article in Elektroniktidningen Feb 2021

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

To be announced

Results

We aim to build robotic matter consisting of thousands or millions of microscale components, so-called “synthetic cells”, that form objects that can autonomously change their shape and material properties.

Realising this objective requires combining four sub-objectives:

  1. Synthesis of the synthetic cells
  2. Thermoreversible bonding of the synthetic cells
  3. Magnetic orientation of synthetic cells with anisotropic properties to program the overall material properties emerging from the resulting cell conglomerate
  4. Transporting synthetic cell material throughout the object to program the overall material shape

Project results

WP1 Synthesis and thermoreversible bonding results:
Investigating the thermoreversible bonding of microparticles.

WP2 Magnetic material programming results:
Investigating integration of small magnets in every cell, and the material property programming by magnetically rotating the anisotropic cells prior to fixation.

WP3 Shape programming results:
Introducing shape programming of solid objects based on the local fluidisation of the object, followed by transport of the fluidised substance through the object by an internal pumping mechanism.

Publications

We like to inspire and share interesting knowledge!

About the project

Objective
This impact project focuses on one of the main areas of the Democritus collaborative project, the digitization of drinking water and wastewater networks. Based on the results of the Democritus project, the objectives of this impact project are the following:

The results will be disseminated through stakeholder workshops, open-source software and accessible video material.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. In the Democritus collaborative project, we have investigated the problems of monitoring and managing these large critical infrastructures with the help of digitization. Within this large area, we focused on three main topics: (a) distributed learning over wireless networks, (b) learning accuracy and security in large systems, and (c) learning for detection and localization with application to water networks.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and RISE, with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation. The project collaborates with SVOA, the Stockholm Water company.

About the project

Objective
The Decision-making in Critical Societal Infrastructures (DEMOCRITUS) project develops methods for monitoring and controlling large-scale infrastructures with the help of digitalization. We design new methods for learning over large datasets, propose networking solutions that support monitoring, learning and control, and construct data-driven models of the monitored physical processes. As an application, Democritus focuses on the water distribution systems, which exhibit many unsolved challenges for future societal systems. We study real-time leak detection, detection and mitigation of possible contamination or attacks, global decision-making while observing local data privacy, and the efficient utilization of smart meters.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. They must also be able to make informed decisions under constraints and in real time – one simple error can have devastating consequences. Despite their technological diversity, the digitalization of these infrastructures can follow a common set of novel design principles.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and from RISE with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation.

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

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Results

Results from three main areas of research include dimensioning of network and learning resources for distributed learning, wireless protocols for supporting machine learning, and methods to localise leaks in water networks from passively collected data. A stakeholder workshop on the project’s topic was organised in 2021 with participation from the public and private sectors, and collaboration with water distribution companies and the KTH Water Centre has been strengthened.

Publications

We like to inspire and share interesting knowledge!

Water distribution networks

DP Souza, R Du, B da Silva Jr, J Mairton, CC Cavalcante, C Fischione, Leakage Detection In Water Distribution Networks: Efficient Training By Data Clustering, IWA World Water Congress & Exhibition 2022.

DP Sousa, R Du, J Mairton Barros da Silva Jr, CC Cavalcante, C. Fischione, “Leakage detection in water distribution networks using machine-learning strategies”, Water Supply 23 (3), 1115-1126, 2023.

L. Lindström, S. Gracy, S. Magnússon and H. Sandberg, “Leakage Localization in Water Distribution Networks: A Model-Based Approach”, European Control Conference, 2022.

M. Mascherpa, I. Haasler, B. Ahlgren, J. Karlsson: “Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information.” Accepted for publication in European Journal of Control.

Cyber-physical systems

M, Zhang, Q. Xu, S Magnússon, R. Pilawa-Podgurski and G. Guo, “Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System”, IEEE Energy Conversion Congress and Exposition, 2022.

D Weinberg, Q Wang, TO Timoudas, C Fischione, “A review of reinforcement learning for controlling Building Energy Systems from a computer science perspective”, Sustainable Cities and Society, 104351,3, 2022.

Bengt Ahlgren. Simulator Framework for Developing Decision-Making Methods for Critical Infrastructure. In 18th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2023), Kristianstad University, Kristianstad, June 14-15, 2023.

Large-scale and distributed learning and optimization

A. Alanwar, A. Berndt, K.H. Johansson and H. Sandberg, “Data-Driven Set-Based Estimation Using Matrix Zonotopes with Set Containment Guarantees”, European Control Conference, 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, M. Johansson, “Optimal convergence rates of totally asynchronous optimization”, IEEE Conference on Decision and Control (CDC), 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, and M. Johansson, “Delay-adaptive step-sizes for asynchronous learning” International Conference on Machine Learning (ICML), 2022.

TO Timoudas, S Zhang, S Magnússon, C Fischione, ”A General Framework to Distribute Iterative Algorithms with Localized Information over Networks”, IEEE Transactions on Automatic Control, 2023.

S. Vaishnav and S. Magnússon, “Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning” 2023 IEEE International Conference on Communications (ICC), Rome, Italy, 2023.

S. Vaishnav and S. Magnússon, “Intelligent Processing of Data Streams on the Edge Using Reinforcement Learning” 2023 IEEE International Conference on Communications Workshops (ICC), Rome, Italy, 2023

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Multi-marginal optimal transport with a tree-structured cost and the Schroedinger bridge problem,” SIAM Journal on Control and Optimization, 59(4), 2428-2453, 2021.

I. Haasler, J. Karlsson, and A. Ringh, “Control and estimation of ensembles via structured optimal transport, A computational approach based on entropy-regularized multi-marginal optimal transport,” IEEE Control Systems Magazine 41 (4), 50-69, 2021.

I. Haasler, A. Ringh, Y. Chen, and J. Karlsson, “Efficient computations of multi-species mean field games via graph-structured optimal transport,” IEEE Conference on Decision and Control, 2021.

J. Fan, I. Haasler, J. Karlsson, and Y. Chen, “On the complexity of the optimal transport problem with graph-structured cost,” AISTATS, 2022.

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport” Accepted for publication in Mathematics of Operations Research.

Networking for/with machine learning

H. Hellström, V. Fodor and C. Fischione, “Over-the-Air Federated Learning with Retransmissions,” IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021.

H. Hellström, V. Fodor, C. Fischione, “Unbiased Over-the-Air Computation via Retransmissions,” IEEE , Global Communications Conference (GLOBECOM), 2022.

S Razavikia, JA Peris, JMB Da Silva, C Fischione, “Blind Asynchronous Over-the-Air Federated Edge Learning”, 2022 IEEE Globecom Workshops (GC Wkshps), 1834-1839, 2, 2022.

H Hellström, JMB da Silva Jr, MM Amiri, M Chen, V Fodor, HV Poor, C. Fischione, “Wireless for machine learning: A survey”, Foundations and Trends® in Signal Processing 15 (4), 290-39916, 2022.

P Park, P Di Marco, C Fischione, Optimized over-the-air computation for wireless control systems, IEEE Communications Letters 26 (2), 2022.

Z Chen, EG Larsson, C Fischione, M Johansson, Y Malitsky, Over-the-Air Computation for Distributed Systems: Something Old and Something New, IEEE Networking Magazine, 2023.

H Hellström, V Fodor, C Fischione, “Federated Learning Over-the-Air by Retransmissions”, IEEE Transactions on Wireless Communications, 2023.

S Razavikia, JMB Silva Jr, C Fischione, “Computing Functions Over-the-Air Using Digital Modulations”, IEEE ICC 2023.

Y Kim, E Al Hakim, J Haraldson, H Eriksson, JMB da Silva, C Fischione, Dynamic clustering in federated learning, ICC 2021-IEEE International Conference on Communications, 1-610, 2021.

R Du, S Magnusson, C Fischione, The Internet of Things as a deep neural network, IEEE Communications Magazine 58 (9), 20-2511, 2020.

R Du, S Magnússon, C Fischione, The IoT as a Deep Neural Network, IEEE Communications Magazine, 2, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Cost-efficient distributed optimization in machine learning over wireless networks, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-77, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Machine learning over networks: co-design of distributed optimization and communications, 2020 IEEE 21st International Workshop on Signal Processing Advances in Communications.

X. Wang, F. S. Samani, and R. Stadler, “Online feature selection for rapid, low-overhead learning in networked systems,” 16th International Conference on Network and Service Management (CNSM 2020). IEEE, 2020.

R. S. Villaca and R. Stadler, “Online learning under resources constraints,” in 2021 17th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2021). IEEE, 2021.

X. Wang, FS. Samani, A. Johnsson,R. Stadler, “Online Feature Selection for Low-overhead Learning in Networked Systems (Demonstration),” In 2021 17th International Conference on Network and Service Management (CNSM) 2021 Oct 25 (pp. 527-529). IEEE, 2021.

X. Wang, R. Stadler, “Online Feature Selection for Efficient Learning in Networked System,” IEEE Transactions on Network and Service Management. 2022 Jun 8.

Network protocol design

P Park, P Di Marco, C Fischione, Wireless for control: Over-the-air controller, IEEE Communications Letters 25 (10), 3437-34411, 2021.

TO Timoudas, R Du, C Fischione, Enabling massive IoT in ambient backscatter communication systems, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-64 2020.

R Du, TO Timoudas, C Fischione, Comparing backscatter communication and energy harvesting in massive IoT networks, IEEE Transactions on Wireless Communications 21 (1), 429-4431, 2021.

P Park, HS Ghadikolaei, C Fischione, Proactive fault-tolerant wireless mesh networks for mission-critical control systems, Journal of Network and Computer Applications 186, 1030824, 2021.

M. Zeng, V. Fodor, Energy minimization for delay constrained mobile edge computing with orthogonal and non-orthogonal multiple access, Ad Hoc Networks, Vol. 98, 2020.

M. Zeng and V. Fodor, “Parallel Processing at the Edge in Dense Wireless Networks,” in IEEE Open Journal of the Communications Society, vol. 3, 2022.

J. A. Peris and V. Fodor, “Modelling multi-cell edge video analytics,” ICC 2022 – IEEE International Conference on Communications, 2022.

J. A. Peris and V. Fodor, “Distributed Join-the-Shortest-Queue with Sparse and Unreliable Information Updates,” ICC 2022 – IEEE International Conference on Communications, 2022.

B. Ahlgren, and K-J Grinnemo, “ZQTRTT: a multipath scheduler for heterogeneous traffic in ICNs based on zero queueing time ratio”, In Proceedings of the 9th ACM Conference on Information-Centric Networking (ICN ’22), 2022.

About the project

Objective
Soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop optimal management practices to maximise the sequestered carbon and crop yield. This project aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, we propose to build a simulator to model and simulate the complex soil-water-plant-atmosphere interactions, which will run on high-performance computing platforms.

Background
Massive simulations using such platforms allow the evaluation of the effects of various management practices under different weather and soil conditions in a timely and cost-effective manner. By formulating the management decision as an RL problem, we can leverage the state-of-the-art algorithms to train management policies, which are expected to maximise the stored organic carbon while maximising the crop yield. The whole system will be tested using data on soil and crops in both the mid-west of the United States and the Mediterranean region. The proposed research has great potential to impact climate change and food security, two of humanity’s most significant challenges.

Crossdisciplinary collaboration
This project is a collaboration between the University of Illinois at Urbana-Champaign, KTH Department of Sustainable Development and Stockholm University, Department of Physical Geography.

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