1.
Blanch, Krister, 1991
(author)
Beyond-application datasets and automated fair benchmarking
2023
Licentiate thesis (other academic/artistic) abstract
Beyond-application perception datasets are generalised datasets that emphasise the fundamental components of good machine perception data. When analysing the history of perception datatsets, notable trends suggest that design of the dataset typically aligns with an application goal. Instead of focusing on a specific application, beyond-application datasets instead look at capturing high-quality, high-volume data from a highly kinematic environment, for the purpose of aiding algorithm development and testing in general. Algorithm benchmarking is a cornerstone of autonomous systems development, and allows developers to demonstrate their results in a comparative manner. However, most benchmarking systems allow developers to use their own hardware or select favourable data. There is also little focus on run time performance and consistency, with benchmarking systems instead showcasing algorithm accuracy. By combining both beyond-application dataset generation and methods for fair benchmarking, there is also the dilemma of how to provide the dataset to developers for this benchmarking, as the result of a high-volume, high-quality dataset generation is a significant increase in dataset size when compared to traditional perception datasets. This thesis presents the first results of attempting the creation of such a dataset. The dataset was built using a maritime platform, selected due to the highly dynamic environment presented on water. The design and initial testing of this platform is detailed, as well as as methods of sensor validation. Continuing, the thesis then presents a method of fair benchmarking, by utilising remote containerisation in a way that allows developers to present their software to the dataset, instead of having to first locally store a copy. To test this dataset and automatic online benchmarking, a number of reference algorithms were required for initial results. Three algorithms were built, using the data from three different sensors captured on the maritime platform. Each algorithm calculates vessel odometry, and the automatic benchmarking system was utilised to show the accuracy and run-time performance of these algorithms. It was found that the containerised approach alleviated data management concerns, prevented inflated accuracy results, and demonstrated precisely how computationally intensive each algorithm was.
2.
Farooqui, Ashfaq, et al.
(author)
On Active Learning for Supervisor Synthesis
2024
In: IEEE Transactions on Automation Science and Engineering. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-5955 .- 1558-3783. ; 21, s. 78-
Journal article (peer-reviewed) abstract
Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study.
3.
Lv, Zhihan, Dr. 1984-, et al.
(author)
Editorial : 5G for Augmented Reality
2022
In: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153.
Journal article (peer-reviewed)
4.
Chen, Dongliang, et al.
(author)
The Scanner of Heterogeneous Traffic Flow in Smart Cities by an Updating Model of Connected and Automated Vehicles
2022
In: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:12, s. 25361-25370
Journal article (peer-reviewed) abstract
The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.
5.
Kebande, Victor R., 1985-, et al.
(author)
A blockchain-based multi-factor authentication model for a cloud-enabled internet of vehicles
2021
In: Sensors. - : MDPI. - 1424-8220. ; 21:18
Journal article (peer-reviewed) abstract
Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
6.
Liu, Yuqi, et al.
(author)
Liquid Digital Twins Based on Magnetic Fluid Toys
2022
In: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - : IEEE. - 9781665484022 ; , s. 988-989
Conference paper (peer-reviewed) abstract
As a new type of functional material, magnetic fluid has both the fluidity of liquid and the magnetic properties of solid magnetic material. By controlling the magnets, one can simulate the effect of manipulating liquids like a sea emperor. This will provide new ideas for the multiverse of the metaverse. Not only that, magnetic fluids also have very important applications in astrophysics, controlled thermonuclear reactions and even the medical industry. Therefore, this paper hopes to provide a control idea for the future application of magnetic fluid by performing Digital Twins simulation of magnetic fluid.
7.
Pelliccione, Patrizio, 1975, et al.
(author)
Beyond connected cars: A systems of systems perspective
2020
In: Science of Computer Programming. - Amsterdam : Elsevier BV. - 0167-6423 .- 1872-7964. ; 191
Journal article (peer-reviewed) abstract
The automotive domain is rapidly changing in the last years. Among the different challenges OEMs (i.e. the vehicle manufacturers) are facing, vehicles are evolving into systems of systems. In fact, over the last years vehicles have evolved from disconnected and “blind” systems to systems that are (i) able to sense the surrounding environment and (ii) connected with other vehicles, the city, pedestrians, cyclists, etc. Future transportation systems can be seen as a System of Systems (SoS). In an SoS, constituent systems, i.e. the units that compose an SoS, can act as standalone systems, but their cooperation enables new emerging and promising scenarios. While this trend creates new opportunities, it also poses a risk to compromise key qualities such as safety, security, and privacy. In this paper we focus on the automotive domain and we investigate how to engineer and architect cars in order to build them as constituents of future transportation systems. Our contribution is an architectural viewpoint for System of Systems, which we demonstrate based on an automotive example. Moreover, we contribute a functional reference architecture for cars as constituents of an SoS. This reference architecture can be considered as an imprinting for the implementations that would be devised in specific projects and contexts. We also point out the necessity for a collaboration among different OEMs and with other relevant stakeholders, such as road authorities and smart cities, to properly engineer systems of systems composed of cars, trucks, roads, pedestrians, etc. This work is realized in the context of two Swedish projects coordinated by Volvo Cars and involving some universities and research centers in Sweden and many suppliers of the OEM, including Autoliv, Arccore, Combitech, Cybercom, Knowit, Prevas, ÅF-Technology, Semcom, and Qamcom.
8.
Zhang, Kewei
(author)
Secure GNSS-based Positioning and Timing : Distance-Decreasing attacks, fault detection and exclusion, and attack detection with the help of opportunistic signals
2021
Doctoral thesis (other academic/artistic) abstract
With trillions of devices connected in large scale systems in a wired or wireless manner, positioning and synchronization become vital. Global Navigation Satellite System (GNSS) is the first choice to provide global coverage for positioning and synchronization services. From small mobile devices to aircraft, from intelligent transportation systems to cellular networks, and from cargo tracking to smart grids, GNSS plays an important role, thus, requiring high reliability and security protection. However, as GNSS signals propagate from satellites to receivers at distance of around 20 000 km, the signal power arriving at the receivers is very low, making the signals easily jammed or overpowered. Another vulnerability stems from that civilian GNSS signals and their specifications are publicly open, so that anyone can craft own signals to spoof GNSS receivers: an adversary forges own GNSS signals and broadcasts them to the victim receiver, to mislead the victim into believing that it is at an adversary desired location or follows a false trajectory, or adjusts its clock to a time dictated by the adversary. Another type of attack is replaying GNSS signals: an adversary transmits a pre-recorded GNSS signal stream to the victim receiver, so that the receiver calculates an erroneous position and time. Recent incidents reported in press show that the GNSS functionalities in a certain area, e.g., Black Sea, have been affected by cyberattacks composed of the above-mentioned attack types. This thesis, thus, studies GNSS vulnerabilities and proposes detection and mitigation methods for GNSS attacks, notably spoofing and replay attacks. We analyze the effectiveness of one important and powerful replay attack, the so-called Distance-decreasing (DD) attacks that were previously investigated for wireless communication systems, on GNSS signals. DD attacks are physical layer attacks, targeting time-of-flight ranging protocols, to shorten the perceived as measured distance between the transmitter and receiver. The attacker first transmits an adversary-chosen data bit to the victim receiver before the signal arrives at the attacker; upon receipt of the GNSS signal, the attacker estimates the data bit based on the early fraction of the bit period, and then switches to transmitting the estimate to the victim receiver. Consequently, the DD signal arrives at the victim receiver earlier than the genuine GNSS signals would have, which in effect shortens the pseudorange measurement between the sender (satellite) and the victim receiver, consequently, affecting the calculated position and time of the receiver. We study how the DD attacks affect the bit error rate (BER) of the received signals at the victim, and analyze its effectiveness, that is, the ability to shorten pseudorange measurements, on different GNSS signals. Several approaches are considered for the attacker to mount a DD attack with high probability of success (without being detected) against a victim receiver, for cryptographically unprotected and protected signals. We analyze the tracking output of the DD signals at the victim receiver and propose a Goodness of Fit (GoF) test and a Generalized Likelihood Ratio Test (GLRT) to detect the attacks. The evaluation of the two tests shows that they are effective, with the result being perhaps more interesting when considering DD attacks against Galileo signals that can be cryptographically protected. Moreover, this thesis investigates the feasibility of validating the authenticity of the GNSS signals with the help of opportunistic signals, which is information readily available in modern communication environments, e.g., 3G, 4G and WiFi. We analyze the time synchronization accuracy of different technologies, e.g., Network Time Protocol (NTP), WiFi and local oscillator, as the basis for detecting a discrepancy with the GNSS-obtained time. Two detection approaches are proposed and one testbench is designed for the evaluation. A synthesized spoofing attack is used to verify the effectiveness of the approaches. Beyond attack detection, we develop algorithms to detect and exclude faulty signals, namely the Clustering-based Solution Separation Algorithm (CSSA) and the Fast Multiple Fault Detection and Exclusion (FM-FDE). They both utilize the redundant available satellites, more than the minimum a GNSS receiver needs for position and time offset calculation. CSSA adopts data clustering to group subsets of positions calculated with different subsets of available satellites. Basically, these positions, calculated with subsets not containing any faulty satellites, should be close to each other, i.e., in a dense area; otherwise they should be scattered. FM-FDE is a more efficient algorithm that uses distances between positions, calculated with fixed-size subsets, as test statistics to detect and exclude faulty satellite signals. As the results show, FM-FDE runs faster than CSSA and other solution-separation fault detection and exclusion algorithms while remaining equally effective.
9.
Rosenstatter, Thomas, 1992, et al.
(author)
REMIND: A Framework for the Resilient Design of Automotive Systems
2020
Book (other academic/artistic) abstract
In the past years, great effort has been spent on enhancing the security and safety of vehicular systems. Current advances in information and communication technology have increased the complexity of these systems and lead to extended functionalities towards self-driving and more connectivity. Unfortunately, these advances open the door for diverse and newly emerging attacks that hamper the security and, thus, the safety of vehicular systems. In this paper, we contribute to supporting the design of resilient automotive systems. We review and analyze scientific literature on resilience techniques, fault tolerance, and dependability. As a result, we present the REMIND resilience framework providing techniques for attack detection, mitigation, recovery, and resilience endurance. Moreover, we provide guidelines on how the REMIND framework can be used against common security threats and attacks and further discuss the trade-offs when applying these guidelines.
10.
David, I., et al.
(author)
Blended modeling in commercial and open-source model-driven software engineering tools: A systematic study
2023
In: Software and Systems Modeling. - : Springer Science and Business Media LLC. - 1619-1366 .- 1619-1374. ; 22, s. 415-447
Journal article (peer-reviewed) abstract
Blended modeling aims to improve the user experience of modeling activities by prioritizing the seamless interaction with models through multiple notations over the consistency of the models. Inconsistency tolerance, thus, becomes an important aspect in such settings. To understand the potential of current commercial and open-source modeling tools to support blended modeling, we have designed and carried out a systematic study. We identify challenges and opportunities in the tooling aspect of blended modeling. Specifically, we investigate the user-facing and implementation-related characteristics of existing modeling tools that already support multiple types of notations and map their support for other blended aspects, such as inconsistency tolerance, and elevated user experience. For the sake of completeness, we have conducted a multivocal study, encompassing an academic review, and grey literature review. We have reviewed nearly 5000 academic papers and nearly 1500 entries of grey literature. We have identified 133 candidate tools, and eventually selected 26 of them to represent the current spectrum of modeling tools.
