The advent of new technologies (such as cyber-physical systems, CPS, and internet of things, IoT) and the advancement in networking (e.g., 5G) create the foundational infrastructure for a new world. In this world, real-life objects are able to interact with each other and with humans to offer boundless opportunities to industry and society – smart city, smart industry, smart energy, etc. This opens up for new opportunities, but also for new challenges. Indeed, in such a dynamic and open system, “change” is the only constant: everything is connected and can suddenly appear, disappear, move around, and take autonomous decisions.
Perpetual change changes everything. Systems are complex and unstable. Instability leads to loss of control, which in turn makes the system untrustworthy. To prevent the cause-and-effect chain, systems shall be provided with proper mechanisms to be resilient and return in balance whenever they face instability. That is, systems shall be able to confront with instability, mitigate uncertainty, and adapt to preserve their goals.
Continuous system-thinking engineering
To deal with perpetual change, EReS investigates continuous system-thinking engineering approaches, enabling the software systems to be resilient and able to accommodate the change. Continuous refers to the fact that the engineering approaches shall be automated and never-ending. System-thinking refers to the fact that the engineering approaches shall be holistic and multi-disciplinary, and considering at the same time all the system constituents, the possible affecting factors, and the properties of interest.
To this end, we ground on well-established theories, methods, and practices, and investigate how to enhance them by leveraging new technologies (e.g., IoT, CPS, digital twins, big data) and techniques (e.g., artificial intelligence, self-adaptation, analytics, formal methods).
In this research context, the specific objective of the EReS Research Lab is to investigate (and experiment with) continuous system-thinking engineering methods, techniques, and tools for architecting, modeling, developing, analyzing, validating, operating, and evolving resilient systems.
Topics of interest
The EReS Research Lab integrates expertise from different research areas such as software engineering, performance engineering, security engineering, safety engineering, and sustainable engineering. Our topics of interest include, but are not limited to:
- Complex systems modeling and simulation
- Design quality, requirements engineering
- Multi-formalisms methodologies for modeling and evaluating systems performance
- Language-based security, threat modeling, and analysis
- Safe autonomy, and cyber-physical security
- Resilience of systems connected to sustainability challenges: e.g., risks connected to the climate emergency
- Energy efficiency in IoT systems
As researchers, our mission is to turn challenges into opportunities. To this end, we actively collaborate with several research groups (both nationally and internationally) and industrial partners.
Connection to Linnaeus University Centre for Data Intensive Sciences and Applications (DISA)
The Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) focuses its efforts on open questions in collection, analysis and utilization of large data sets. With its core in computer science, it takes a multidisciplinary approach and collaborates with researchers from all faculties at the university.
Considering both the cyber and physical factors in the design of CPS raises many challenges as they stress the known methods, processes, and tools to their extreme, and introduce new difficulties to deal with. While on the one hand, CPSs provide new opportunities, on the other hand, the integration of cyber and physical components increases the complexity of the overall system, as it seriously complicates the design of systems delivering high-quality aspects – i.e., performance, safety, security, sustainability (i.e., near-zero power consumption), scalability, usability.
In this context, EReS is part of the DISA Smart Industry Group (SIG), an interdisciplinary research initiative in collaboration with the Department of mechanical engineering. SIG investigates data-driven approaches for (i) inferring implicit CPS models from data (data-driven discovery), and (ii) estimating and predicting CPS quality using models and real-time data (data-driven analysis).
The overall objective is to leverage data-driven approaches for improving production processes and products. For example, a production system can automatically adjust to desired production level with optimal logistics and flow at lowest cost, whereas a product can automatically reconfigure with respect to operation and human factors.
Models (usually expressed in terms of laws and equations) facilitate the understanding of complex phenomena and allow for systems analysis. However, CPSs have proved resistant to explicit modeling due to their intrinsic complexity arising from the combination of cyber and physical components and the interaction between them.
To this end, the main objective of SIG is to investigate data-driven methodologies for facilitating the data-to-discovery process of implicit CPS Models. Specifically, data-driven methodologies would allow us to learn and infer implicit models from the observation of raw data. These methodologies have the advantage of (i) testing correlations between different variables and observations, and (ii) learning unforeseen patterns in systems and discovering new or hidden implicit behaviors.
Model-based analysis facilitates the evaluation of complex systems and allows for estimating and predicting systems quality. However, it is well known that the computational complexity of model-based analysis techniques is one of the key challenges in complex system validation and evaluation.
To this end, SIG aims at applying data-driven analysis (e.g., machine learning) to enable online model-based estimation and prediction of quality aspects, e.g., performance, security, maintenance.
Connection to Smarter Systems
Smarter Systems is a complete knowledge environment focusing on the systems and challenges of the future. It addresses challenges together with its partners through education and research initiatives in collaboration.
“Perpetual change” changes everything. Smarter systems are complex and unstable. Instability leads to loss of control, which in turn makes the system untrustworthy. To prevent this cause-and-effect chain, smarter systems shall be provided with proper mechanisms to be resilient and return in balance whenever they face instability. That is, software systems shall be able to confront instability, mitigate uncertainty, and accommodate changes to preserve their goals.
To deal with perpetual change, EReS investigates continuous (model-based) system engineering approaches enabling smarter systems to be resilient and able to accommodate the change. Continuous refers to the fact that the engineering approaches shall be automated and never-ending, whereas model-based system engineering refers to the fact that the engineering approaches shall be model-based, holistic and interdisciplinary. Indeed, able to consider at the same time all the system constituents, the possible affecting factors, and the properties of interest.
However, model-based systems engineering approaches have several difficulties, including (but not limited to): the integration of different modeling formalisms and analysis techniques, ensuring consistency and completeness, integrating human behaviours, as well as considering uncertainty as an integrated part of the approach. To this extent, EReS aims at tackling the following grand challenges:
1. Modeling and analysis for resilience (KO1)
- How to specify resilience?
- E.g., goals, requirements, qualities, uncertainty ...
- How to model resilience?
- E.g., structure, behavior, uncertainty
- How to assess resilience?
- E.g., quality models, metrics, techniques (e.g., testing, simulation, formal methods, …)
2. Develop for resilience (KO2)
- How to achieve resilience?
- Design (e.g., principles, practices, patterns)
- Development (e.g., languages, tool, platforms)
- Operate (e.g., infrastructures, technologies)
- Decision-making process (e.g., human-in-the-loop, autonomous, trustable, explainable)
3. Resilient applications (KO1, KO2)
- How to exploit resilience (industry/society level)?
- Smart Cities, Green Economy, Industry 4.0
 KO1: To assure product capabilities in uncertain conditions.
 KO2: To devise engineering processes for perpetual adaptation and evolution.
Connection to education
EReS Research Lab is responsible for the following set of courses:
- 1DT305, Introduction to Applied Internet of Things, 7.5 credits
- 1DV517, Formal Languages and Logic, 7.5 credits
- 2ME302, Research challenges in Media Technology, 7.5 credits
- 2DT393, Reliability in Embedded Systems (Fredrik Ahlgren)
- 2DV515, Web Intelligence, 7.5 credits
- 2DV517, Deployment Infrastructures (Diego Perez)
- 2DV611, Continuous Delivery (Diego Perez)
- 4ME307, Internet Architectures, 7.5 credits
- 4DV650, Systems Simulation and Modeling, 5 credits
- 4DV651, Project in Model-based development, 10 credits
- 4DV701, Formal Methods, 5 credits
Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) encourages and supports seed projects. Seed projects are intended to promote and nurture excellence research, development and innovation within data intensive sciences and applications with cross-discipline collaboration.
Seed projects at Engineering Resilient Systems
Ongoing research projects
Doctoral project: Digital Twin as a Service (DTaaS) This doctoral project targets to work on Digital Twin, with integration to data intensive sources.
Doctoral project: Realising smarter organization by developing Digital Twin of the Organization A Digital Twin of an Organization (DTO) as a software component proposes a live model of an…
Project: Aligning Architectures for Digital Twin of the Organization (Aladino) This project aims at establishing a set of sound engineering methodologies, methods and tools for modeling, evaluating,…
Project: Climate neutral Växjö 2030 This project is a research collaboration with Växjö municipality for achieving a climate neutral Växjö in 2030 and how digital technologies can support this goal…
Project: Designing digital technologies for supporting energy-related behavior change in the kitchen This project aims to design, develop and evaluate digital behavioral change tools. The goal is to…
Project: IoT lab for SME This project will increase knowledge of the possibilities using Internet of Things (IoT), and develop new ideas and products, among small and medium-sized enterprises (SME) in…
Project: Roadmaps for AI Integration in the Rail Sector (RAILS) The overall objective of the RAILS research project is to investigate the potential of artificial intelligence (AI) approaches in the…
Project: Smart storage solutions in the fridge of the future to reduce food waste About one third of the food production in the world is thrown away, which is one of the largest single sources of…
Past research projects
Project: Provably Secure Self-Protecting Systems (PROSSES) The PROSSES project will result in techniques and tools to create a protecting layer for software systems against attacks from the Internet.
Project: Software Technology for Self-Adaptive Systems The purpose of this project is to increase the engineering efficiency of self-adaptive systems. The development, maintenance and operation of…
- Charilaos Skandylas Doctoral student
- Diego Perez Palacin Senior lecturer
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- Faiz Ul Muram Associate senior lecturer
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- Farid Edrisi Doctoral student
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- Francesco Flammini Associate Professor
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- Francis Palma
- Fredrik Ahlgren Senior lecturer
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- Jorge Luis Zapico Senior lecturer
- Mauro Caporuscio Professor
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- Mehdi Saman Azari Doctoral student
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- Nadeem Abbas Senior lecturer
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- Narges Khakpour Associate Professor
- Samuele Giussani Doctoral student
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