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  • 151.
    Ayala, Inmaculada
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gallina, Barbara
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Towards Tool-based Security-informed Safety Oriented Process Line Engineering2016Inngår i: 1st International workshop on Interplay of Security, Safety and System/Software Architecture ISSA-2016, 2016, artikkel-id 38Konferansepaper (Fagfellevurdert)
    Abstract [en]

    For the purpose of certification, manufactures of nowadays highly connected safety-critical systems are expected to en- gineer their systems according to well-defined engineering processes in compliance with safety and security standards. Certification is an extremely expensive and time-consuming process. Since safety and security standards exhibit a certain degree of commonality, certification-related artifacts (e.g., process models) should to some extent be reusable. To en- able systematic reuse and customization of process infor- mation, in this paper we further develop security-informed safety-oriented process line engineering (i.e., engineering of sets of processes including security and safety concerns). More specifically, first we consider three tool-supported ap- proaches for process-related commonality and variability man- agement and we apply them to limited but meaningful por- tions of safety and security standards within airworthiness. Then, we discuss our findings. Finally, we draw our conclu- sions and sketch future work.

  • 152.
    Aysan, Huseyin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Bate, Iain
    University of York.
    Graydon, Patrick
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Improving Reliability of Real-Time Systems through Value and Time Voting2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Critical systems often use N-modular redundancy to tolerate faults in subsystems. Traditional approaches to N-modular redundancy in distributed, loosely-synchronised, real-time systems handle time and value errors separately: a voter detects value errors, while watchdog-based health monitoring detects timing errors. In prior work, we proposed the integrated Voting on Time and Value (VTV) strategy, which allows both timing and value errors to be detected simultaneously. In this paper, we show how VTV can be harnessed as part of an overall fault tolerance strategy and evaluate its performance using a well-known control application, the Inverted Pendulum. Through extensive simulations, we compare the performance of Inverted Pendulum systems which employs VTV and alternative voting strategies to demonstrate that VTV better tolerates well-recognised faults in this realistically complex control problem.

  • 153.
    Aysan, Huseyin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Dobrin, Radu
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Punnekkat, Susikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Schedulability guarantees for dependable distributed real-time systems under error bursts2013Inngår i: Advances in Intelligent Systems and Computing, Springer Verlag , 2013, Vol. 187, s. 393-406Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In dependable embedded real-time systems, typically built of computing nodes exchanging messages over reliability-constrained networks, the provision of schedulability guarantees for task and message sets under realistic fault and error assumptions is an essential requirement, though complex and tricky to achieve. An important factor to be considered in this context is the random nature of occurrences of faults and errors, which, if addressed in the traditional schedulability analysis by assuming a rigid worst-case occurrence scenario, may lead to inaccurate results. In this work we propose a framework for end-to-end probabilistic schedulability analysis for real-time tasks exchanging messages over Controller Area Network under stochastic errors.

  • 154.
    Aysan, Hüseyin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Dobrin, Radu
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Probabilistic schedulability analysis for fault tolerant tasks under stochastic error occurrences2013Inngår i: 19th International Conference on Control Systems and Computer Science, CSCS 2013: Proceedings, 2013, s. 113-120Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In dependable real-time systems, provision of schedulability guarantees for task sets under realistic fault and error assumptions is an essential requirement, though complex and tricky to achieve. An important factor to be considered in this context is the random nature of occurrences of faults and errors, which, if addressed in the traditional schedulability analysis by assuming a rigid worst case occurrence scenario, may lead to inaccurate results. In this paper we first propose a stochastic fault and error model which has the capability of modeling error bursts in lieu of the commonly used simplistic error assumptions in processor scheduling. We then present a novel schedulability analysis that accounts for a range of worst case scenarios generated by stochastic error burst occurrences on the response times of tasks scheduled under the fixed priority scheduling (FPS) policy. Finally, we describe a methodology for the calculation of probabilistic schedulability guarantees as a weighted sum of the conditional probabilities of schedulability under specified error burst characteristics.

  • 155.
    Badampudi, D.
    et al.
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Wnuk, K.
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Wohlin, C.
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Franke, U.
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Smite, D.
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Cicchetti, Antonio
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A decision-making process-line for selection of software asset origins and components2018Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 135, s. 88-104Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Selecting sourcing options for software assets and components is an important process that helps companies to gain and keep their competitive advantage. The sourcing options include: in-house, COTS, open source and outsourcing. The objective of this paper is to further refine, extend and validate a solution presented in our previous work. The refinement includes a set of decision-making activities, which are described in the form of a process-line that can be used by decision-makers to build their specific decision-making process. We conducted five case studies in three companies to validate the coverage of the set of decision-making activities. The solution in our previous work was validated in two cases in the first two companies. In the validation, it was observed that no activity in the proposed set was perceived to be missing, although not all activities were conducted and the activities that were conducted were not executed in a specific order. Therefore, the refinement of the solution into a process-line approach increases the flexibility and hence it is better in capturing the differences in the decision-making processes observed in the case studies. The applicability of the process-line was then validated in three case studies in a third company. 

  • 156.
    Bagheri, M.
    et al.
    Sharif University of Technology, Tehran, Iran.
    Khamespanah, E.
    Sharif University of Technology, Tehran, Iran.
    Sirjani, Marjan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Reykjavik University, Reykjavik, Iceland.
    Movaghar, A.
    Sharif University of Technology, Tehran, Iran.
    Lee, A. E.
    University of California at Berkeley.
    Runtime compositional analysis of track-based traffic control systems2017Inngår i: ACM SIGBED Review, ISSN 1551-3688, Vol. 14, nr 3, s. 38-39Artikkel i tidsskrift (Fagfellevurdert)
  • 157.
    Bagheri, M.
    et al.
    Sharif University of Technology, Tehran, Iran.
    Sirjani, Marjan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Reykjavik University, Reykjavik, Iceland.
    Khamespanah, E.
    Reykjavik University, Reykjavik, Iceland.
    Khakpour, N.
    Linnaeus University, Växjö Campus, Sweden.
    Akkaya, I.
    University of California at Berkeley, CA, United States.
    Movaghar, A.
    Sharif University of Technology, Tehran, Iran.
    Lee, E. A.
    University of California at Berkeley, CA, United States.
    Coordinated actor model of self-adaptive track-based traffic control systems2018Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 143, s. 116-139Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Self-adaptation is a well-known technique to handle growing complexities of software systems, where a system autonomously adapts itself in response to changes in a dynamic and unpredictable environment. With the increasing need for developing self-adaptive systems, providing a model and an implementation platform to facilitate integration of adaptation mechanisms into the systems and assuring their safety and quality is crucial. In this paper, we target Track-based Traffic Control Systems (TTCSs) in which the traffic flows through pre-specified sub-tracks and is coordinated by a traffic controller. We introduce a coordinated actor model to design self-adaptive TTCSs and provide a general mapping between various TTCSs and the coordinated actor model. The coordinated actor model is extended to build large-scale self-adaptive TTCSs in a decentralized setting. We also discuss the benefits of using Ptolemy II as a framework for model-based development of large-scale self-adaptive systems that supports designing multiple hierarchical MAPE-K feedback loops interacting with each other. We propose a template based on the coordinated actor model to design a self-adaptive TTCS in Ptolemy II that can be instantiated for various TTCSs. We enhance the proposed template with a predictive adaptation feature. We illustrate applicability of the coordinated actor model and consequently the proposed template by designing two real-life case studies in the domains of air traffic control systems and railway traffic control systems in Ptolemy II. 

  • 158.
    Bagheri, Maryam
    et al.
    Sharif University of Technology, Iran.
    Akkaya, Ilge
    University of California at Berkley, US.
    Khamespanah, Ehsan
    Reykjavik University, Iceland.
    Khakpour, Narges
    Linnaeus University, Sweden.
    Sirjani, Marjan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Movaghar, Ali
    Sharif University of Technology, Iran.
    Lee, Edward
    University of California at Berkley, US.
    Coordinated Actors for Reliable Self-Adaptive Systems2017Inngår i: The 13th International Conference on Formal Aspects of Component Software FACS 2016, 2017, Vol. 10231, s. 241-259Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Self-adaptive systems are systems that automatically adapt in response to environmental and internal changes, such as possible failures and variations in resource availability. Such systems are often realized by a MAPE-K feedback loop, where Monitor, Analyze, Plan and Execute components have access to a runtime model of the system and environment which is kept in the Knowledge component. In order to provide guarantees on the correctness of a self-adaptive system at runtime, the MAPE-K feedback loop needs to be extended with assurance techniques. To address this issue, we propose a coordinated actor-based approach to build a reusable and scalable model@runtime for self-adaptive systems in the domain of track-based traffic control systems. We demonstrate the approach by implementing an automated Air Traffic Control system (ATC) using Ptolemy tool.We compare different adaptation policies on the ATC model based on performance metrics and analyze combination of policies in different configurations of the model. We enriched our framework with runtime performance analysis such that for any unexpected change, subsequent behavior of the model is predicted and results are used for adaptation at the change-point. Moreover, the developed framework enables checking safety properties at runtime.

  • 159.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Gholamhosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Connolly, M. J.
    University of Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Advanced decision support system for older adults2015Inngår i: Studies in Health Technology and Informatics, vol. 211, 2015, s. 235-240Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Decision support systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia and to generate early warnings. 

  • 160.
    Baig, M. M.
    et al.
    Auckland University of Technology, New Zealand.
    GholamHosseini, H.
    Auckland University of Technology, New Zealand.
    Moqeem, A. A.
    Auckland University of Technology, New Zealand.
    Mirza, F.
    Auckland University of Technology, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption2017Inngår i: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, nr 7, artikkel-id 115Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology. 

  • 161.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Hosseini, H. G.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Machine learning-based clinical decision support system for early diagnosis from real-time physiological data2016Inngår i: Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc. , 2016, s. 2943-2946, artikkel-id 7848584Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This research aims to design a self-organizing decision support system for early diagnosis of key physiological events. The proposed system consists of pre-processing, clustering and diagnostic system, based on self-organizing fuzzy logic modeling. The clustering technique was employed with empirical pattern analysis, particularly when the information available is incomplete or the data model is affected by vagueness, which is mostly the case with medical/clinical data. Clustering module can be viewed as unsupervised learning from a given dataset. This module partitions the patient vital signs to identify the key relationships, patterns and clusters among the medical data. Secondly, it uses self-organizing fuzzy logic modeling for early symptom and event detection. Based on the clustering outcome, when detecting abnormal signs, a high level of agreement was observed between system interpretation and human expert diagnosis of the physiological events and signs. © 2016 IEEE.

  • 162.
    Baig, M.M.
    et al.
    Auckland University of Technology.
    GholamHosseini, H.
    Auckland University of Technology.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care2015Inngår i: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2015, s. 1215-1218Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet computers which are increasingly integrating into the healthcare mainstream as clinical decision support systems. Despite the benefits of table-based healthcare applications, there are concerns around the accuracy, security and stability of such applications. In this study, we developed five tablet-based application screens for remote patient monitoring at hospital care settings and identified related issues and challenges. The ultimate aim of this research is to integrate decision support algorithms into the monitoring system in order to improve inpatient care and the effectiveness of such applications.

  • 163.
    Bakhshi Valojerdi, Zeinab
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rodriguez-Navas, Guillermo
    Nokia Bell Labs, Israel.
    A Preliminary Roadmap for Dependability Research in Fog Computing2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fog computing aims to support novel real-time applications byextending cloud resources to the network edge. This technologyis highly heterogeneous and comprises a wide variety of devicesinterconnected through the so-called fog layer. Compared to tra-ditional cloud infrastructure, fog presents more varied reliabilitychallenges, due to its constrained resources and mobility of nodes.This paper summarizes current research efforts on fault toleranceand dependability in fog computing and identifies less investigatedopen problems, which constitute interesting research directions tomake fogs more dependable.

  • 164.
    Bakhshi Valojerdi, Zeinab
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rodriguez-Navas, Guillermo
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hansson, Hans
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Dependable Fog Computing: A Systematic Literature Review2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fog computing has been recently introduced to bridge the gap between cloud resources and the network edge. Fog enables low latency and location awareness, which is considered instrumental for the realization of IoT, but also faces reliability and dependability issues due to node mobility and resource constraints. This paper focuses on the latter, and surveys the state of the art concerning dependability and fog computing, by means of a systematic literature review. Our findings show the growing interest in the topic but the relative immaturity of the technology, without any leading research group. Two problems have attracted special interest: guaranteeing reliable data storage/collection in systems with unreliable and untrusted nodes, and guaranteeing efficient task allocation in the presence of varying computing load. Redundancy-based techniques, both static and dynamic, dominate the architectures of such systems. Reliability, availability and QoS are the most important dependability requirements for fog, whereas aspects such as safety and security, and their important interplay, have not been investigated in depth.

  • 165.
    Bakhshi, Zeynab
    et al.
    RighTel, Iran.
    Balador, Ali
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE SICS, Västerås, Sweden.
    Mustafa, Jawad
    RISE SICS, Västerås, Sweden.
    Industrial IoT Security Threats and Concerns by Considering CISCO and Microsoft IoT reference Models2018Inngår i: IEEE WCNCW 2018 IEEE WCNCW 2018: 2018 IEEE Wireless Communications and Networking Conference Workshops, 2018, s. 173-178Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper investigates security concerns and issues for Industrial Internet of Things (IIoT). The IIoT is an emerging transformation, bringing great values to every industry. Although this rapid alter in industries create values, but there are concerns about security issues, most of which would be still unknown due to the novelty of this platform. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this paper attempts to provide a list of security threats and issues on the cloud-side layer of IoT, which consists of data accumulation and abstraction levels. For this reason, we choose Cisco and Microsoft Azure IoT Architecture as reference models. Then, two layers of Cisco reference architecture model have been chosen to be investigated for their security issues. Finally, consideration of security issues has been briefly explained.

  • 166.
    Balador, Ali
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE SICS, Vadies, Sweden.
    Bai, C.
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Sedighi, F.
    Niroo Research Institute, Iran.
    A Comparison of Decentralized Congestion Control Algorithms for Multiplatooning Communications2019Inngår i: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, s. 674-680Konferansepaper (Fagfellevurdert)
    Abstract [en]

    To improve traffic safety, many Cooperative Intelligent Transportation Systems (C-ITS) applications rely on exchange of periodic safety messages between vehicles. However, as the number of connected vehicles increases, control of channel congestion becomes a bottleneck for achieving high throughput. Without a suitable congestion control method, safety critical messages such as Cooperative Awareness Messages (CAMs) may not be delivered on time in high vehicle density scenarios that can lead to dangerous situations which can threaten people's health or even life. The Decentralized Congestion Control (DCC) algorithm defined by European Telecommunications Standards Institute (ETSI), becomes a vital component of C-ITS applications to keep channel load under control and below a predefined threshold level. In this paper, we aim to analyze and evaluate the performance of a number of DCC protocols including ETSI DCC by providing a comparison between them for the multiplatooning application by using several widely-used evaluation metrics.

  • 167.
    Balador, Ali
    et al.
    SICS Swedish ICT Västerås AB, Sweden.
    Böhm, Annette
    Halmstad University, Sweden.
    Uhlemann, Elisabeth
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Calafate, Carlos T.
    Universitat Politecnica de Valencia, Spain.
    Cano, Juan-Carlos
    Universitat Politecnica de Valencia, Spain.
    A Reliable and Efficient Token-Based MAC Protocol for Platooning Applications2016Inngår i: 12th Swedish National Computer Networking Workshop SNCNW 2016, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Platooning is both a challenging and rewarding application. Challenging since strict timing and reliability requirements are imposed by the distributed control system required to operate the platoon. Rewarding since considerable fuel reductions are possible. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However, TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if retransmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability compared to pure 802.11p.

  • 168.
    Balador, Ali
    et al.
    Universitat Politecnica de Valencia, Spain.
    Böhm, Annette
    Halmstad University, Sweden.
    Uhlemann, Elisabeth
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Calafate, Carlos T.
    Universitat Politecnica de Valencia, Spain.
    Cano, Juan-Carlos
    Universitat Politecnica de Valencia, Spain.
    A reliable token-based MAC protocol for delay sensitive platooning applications2015Inngår i: 2015 IEEE 82nd Vehicular Technology Conference, VTC Fall 2015 - Proceedings, Boston, MA, United States, 2015, s. Article number 7390813-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Platooning is both a challenging and rewarding application. Challenging since strict timing and reliability requirements are imposed by the distributed control system required to operate the platoon. Rewarding since considerable fuel reductions are possible. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However, TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if retransmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability considerably compared to pure 802.11p.

  • 169.
    Balador, Ali
    et al.
    Polytechnic University of Valencia, Valencia, Spain.
    Böhm, Annette
    Halmstad Universit, Sweden.
    Uhlemann, Elisabeth
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Calafate, Carlos T.
    Polytechnic University of Valencia, Valencia, Spain.
    Ji, Yusheng
    National Institute of Informatics, Tokyo, Japan.
    Cano, Juan-Carlos
    Polytechnic University of Valencia, Valencia, Spain.
    Manzoni, Pietro
    Polytechnic University of Valencia, Valencia, Spain.
    An Efficient MAC Protocol for vehicle platooning in automated highway systems2015Inngår i: Jornadas Sarteco 2015 JS 2015, Cordoba, Spain, 2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Lately, all the top truck manufacturers are investing considerable resources in the research and development of platooning systems which would allow vehicles to save fuel and improve safety by travelling in a close-following manner. The platoon-ing system requires frequent and reliable vehicle-to-vehicle communications. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However , TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if re-transmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability considerably compared to pure 802.11p.

  • 170.
    Balador, Ali
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Kouba, A.
    Polytechnic Institute of Porto, Porto 4249-015, Portugal.
    Cassioli, D.
    University of L'Aquila, L'Aquila, 67100, Italy.
    Foukalas, F.
    Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
    Severino, R.
    Polytechnic Institute of Porto, Porto 4249-015, Portugal.
    Stepanova, D.
    Finnish Meteorological Institute, 99600 Sodankylä, Finland.
    Agosta, G.
    Politecnico di Milano ,Via G. Ponzio 32, Milano, I-20133, Italy.
    Xie, J.
    Group Technology & Research, DNV GL, Veritasveien 1, Norway.
    Pomante, L.
    University of L'Aquila, L'Aquila, 67100, Italy.
    Mongelli, M.
    CNR-IEIIT ,via De Marini 6, Genova, 16149, Italy.
    Pierini, P.
    Intecs S.p.A., Pisa, 56121, Italy.
    Petersen, S.
    SINTEF ICT, Trondheim, 7465, Norway.
    Sukuvaara, T.
    Finnish Meteorological Institute, 99600 Sodankylä, Finland.
    Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems2018Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, nr 11, artikkel-id 4075Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

  • 171.
    Balador, Ali
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Uhlemann, Elisabeth
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Calafate, C. T.
    Universitat Politècnica de València, València, Spain.
    Cano, J. -C
    Universitat Politècnica de València, València, Spain.
    Supporting beacon and event-driven messages in vehicular platoons through token-based strategies2018Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, nr 4, artikkel-id 955Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications. 

  • 172.
    Balasubramanian, S. M. N.
    et al.
    Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    Afshar, Sara
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gai, P.
    Evidence Srl, Pisa, Italy.
    Behnam, Moris
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    J. Bril, Reinder
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Practical challenges for FSLM2019Inngår i: Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, s. 238-239, artikkel-id 8607257Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The flexible spin-lock model (FSLM) unifies suspension-based and spin-based resource access protocols for partitioned fixed-priority preemptive scheduling based real-time multi-core platforms. Recent work has been done in defining the protocol for FSLM, providing schedulability analysis, and investigating the practical consequences of the theoretical model. FSLM complies to the AUTOSAR standard for the automotive industry, and prototype implementations of FSLM in the OSEK/VDX-complaint Erika Enterprise Real-Time Operating System have been realized. In this paper, we briefly describe some practical challenges to improve efficiency and generality. 

  • 173.
    Balasubramanian, S.M.N
    et al.
    Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    Afshar, Sara Zargari
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Behnam, Moris
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gai, Paolo
    Evidence Srl, Pisa, Italy.
    Bril, Reinder J.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    A dual shared stack for FSLM in Erika enterprise2017Inngår i: The 23rd IEEE International Conference on Embedded and Real-Time Computing Systems and Applications - WiP Session RTCSA'17, 2017Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recently, the flexible spin-lock model (FSLM) has been introduced, unifying spin-based and suspension-based resource sharing protocols for real-time multi-core platforms. Unlike the multiprocessor stack resource policy (MSRP), FSLM doesn’t allow tasks on a core to share a single stack, however. In this paper, we present a hypothesis claiming that for a restricted range of spin-lock priorities, FSLM requires only two stacks. We briefly describe our implementation of a dual stack for FSLM in the Erika Enterprise RTOS as instantiated on an Altera Nios II platform using 4 soft-core processors.

  • 174.
    Balasubramanian, S.M.N
    et al.
    Tech Univ Eindhoven, Eindhoven, Netherlands.
    Afshar, Sara Zargari
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gai, Paolo
    Evidence Srl, Pisa, Italy.
    Behnam, Moris
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    J. Bril, Reinder
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Tech Univ Eindhoven, Eindhoven, Netherlands.
    Incorporating implementation overheads in the analysis for the flexible spin-lock model2017Inngår i: IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2017, s. 411-8418Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The flexible spin-lock model (FSLM) unifies suspension-based and spin-based resource sharing protocols for partitioned fixed-priority preemptive scheduling based real-time multiprocessor platforms. Recent work has been done in defining the protocol for FSLM and providing a schedulability analysis without accounting for the implementation overheads. In this paper, we extend the analysis for FSLM with implementation overheads. Utilizing an initial implementation of FSLM in the OSEK/VDX-compliant Erika Enterprise RTOS on an Altera Nios II platform using 4 soft-core processors, we present an improved implementation. Given the design of the implementation, the overheads are characterized and incorporated in specific terms of the existing analysis. The paper also supplements the analysis with measurement results, enabling an analytical comparison of FSLM with the natively provided multiprocessor stack resource policy (MSRP), which may serve as a guideline for the choice of FSLM or MSRP for a specific application.

  • 175.
    Ballesteros, A.
    et al.
    DMI, Universitat de les Illes Balears, Spain.
    Proenza, J.
    DMI, Universitat de les Illes Balears, Spain.
    Gessner, D.
    DMI, Universitat de les Illes Balears, Spain.
    Rodriguez-Navas, Guillermo
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sauter, T.
    Danube University Krems, Austria.
    Achieving elementary cycle synchronization between masters in the flexible time-triggered replicated star for ethernet2014Inngår i: 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 2014, s. Article number 7005335-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    For a distributed embedded system (DES) to operate continuously in a dynamic environment, it must be flexible and highly reliable. This applies in particular to its communication subsystem. The Flexible Time-Triggered Replicated Star for Ethernet (FTTRS) aims at providing such a subsystem by means of a highly-reliable switched-Ethernet architecture based on the Flexible Time-Triggered paradigm (FTT), a master/slave communication paradigm where the master periodically polls the slaves using so-called trigger messages (TMs). In particular, FTTRS interconnects nodes by redundant communication paths provided by two switches, each embedding an FTT master that manages the communication. This allows FTTRS to tolerate the failure of one switch without interrupting the communication as long as the masters are replica determinate, i.e., provide identical service to the slaves. The master replica determinism entails the masters broadcasting their TMs in a lockstep fashion: when one master broadcasts a TM, the other should do the same quasi-simultaneously. In this paper we present a solution inspired by the Precision Time Protocol (PTP) for achieving this lockstep transmission and preliminary results showing the precision with which we can synchronize the masters on a software prototype.

  • 176.
    Ballesteros, Joaquin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tudela, Alberto J.
    University of Malaga, Malaga, Spain.
    Caro-Romero, J. R.
    University of Malaga, Malaga, Spain.
    Urdiales, C.
    University of Malaga, Malaga, Spain.
    Weight-Bearing Estimation for Cane Users by Using Onboard Sensors2019Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 3Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. However, continuous gait analysis is difficult to achieve due to technical limitations, namely the need for specific hardware and constraints on time and test environment to acquire reliable data. Wearables may provide a solution if users carry them most of the time they are walking. We propose to add sensors to walking canes to assess user's mobility. Canes are frequently used by people who cannot completely support their own weight due to pain or balance issues. Furthermore, in absence of neurological disorders, the load on the cane is correlated with the user condition. Sensorized canes already exist, but often rely on expensive sensors and major device modifications are required. Thus, the number of potential users is severely limited. In this work, we propose an affordable module for load monitoring so that it can be widely used as a screening tool. The main advantages of our module are: (i) it can be deployed in any standard cane with minimal changes that do not affect ergonomics; (ii) it can be used every day, anywhere for long-term monitoring. We have validated our prototype with 10 different elderly volunteers that required a cane to walk, either for balance or partial weight bearing. Volunteers were asked to complete a 10 m test and, then, to move freely for an extra minute. The load peaks on the cane, corresponding to maximum support instants during the gait cycle, were measured while they moved. For validation, we calculated their gait speed using a chronometer during the 10 m test, as it is reportedly related to their condition. The correlation between speed (condition) and load results proves that our module provides meaningful information for screening. In conclusion, our module monitors support in a continuous, unsupervised, nonintrusive way during users' daily routines, plus only mechanical adjustment (cane height) is needed to change from one user to another.

  • 177.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data2015Inngår i: 8th International Conference on Health Informatics HEALTHINF, Lisbon, Portugal, 2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.

  • 178.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals2015Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.

    This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.

    For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 

  • 179.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.

    This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.

    Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.

    This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.

  • 180.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Linköping, SE, Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Automatic driver sleepiness detection using EEG, EOG and contextual information2019Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, s. 121-135Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.

  • 181.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahlström, Christer
    MFT, Linköping Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Automated EEG Artifact Handling with Application in Driver Monitoring2017Inngår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, nr 5, s. 1350-1361Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

  • 182.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Classifying drivers' cognitive load using EEG signals2017Inngår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 237, s. 99-106Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy. 

  • 183.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring2018Inngår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 26-33Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

  • 184.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual dataInngår i: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.

  • 185.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Review on Machine Learning Algorithms in Handling EEG Artifacts2014Inngår i: The Swedish AI Society (SAIS) Workshop SAIS, 14, 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

  • 186.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Clustering based Approach for Automated EEG Artifacts Handling2015Inngår i: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, s. 7-16Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Electroencephalogram (EEG), measures the neural activity of the central nervous system, which is widely used in diagnosing brain activity and therefore plays a vital role in clinical and Brain-Computer Interface application. However, analysis of EEG signal is often complex since the signal recoding often contaminates with noises or artifacts such as ocular and muscle artifacts, which could mislead the diagnosis result. Therefore, to identify the artifacts from the EEG signal and handle it in a proper way is becoming an important and interesting research area. This paper presents an automated EEG artifacts handling approach, where it combines Independent Component Analysis (ICA) with a 2nd order clustering approach. Here, the 2nd order clustering approach combines the Hierarchical and Gaussian Picture Model clustering algorithm. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where the clean EEG signal shows acceptable considering visual inspection.

  • 187.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. IS (Embedded Systems).
    Driver’s State Monitoring: A Case Study on Big Data Analytics2016Inngår i: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, s. 145-147Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Driver's distraction, inattention, sleepiness, stress, etc. are identified as causal factors of vehicle crashes and accidents. Today, we know that physiological signals are convenient and reliable measures of driver’s impairments. Heterogeneous sensors are generating vast amount of signals, which need to be handled and analyzed in a big data scenario. Here, we propose a big data analytics approach for driver state monitoring using heterogeneous data that are coming from multiple sources, i.e., physiological signals along with vehicular data and contextual information. These data are processed and analyzed to aware impaired vehicle drivers.

  • 188.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering2017Inngår i: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng., Springer Verlag , 2017, s. 144-148Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Billions of interconnected neurons are the building block of the human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signals, recorded signals often contaminate with undesired physiological signals other than the cerebral signal that is referred to as the EEG artifacts such as the ocular or the muscle artifacts. Therefore, identification and handling of artifacts in the EEG signals in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, combining Wavelet transform, Independent Component Analysis (ICA), and Hierarchical clustering. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to the result, the proposed approach identified artifacts in the EEG signals effectively and after handling artifacts EEG signals showed acceptable considering visual inspection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

  • 189.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchical clustering2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Billions of interconnected neurons are the building block of human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signal, recorded signal often contaminate with undesired physiological signals other than cerebral signal that refers to as EEG artifacts such as ocular or muscle artifacts. Therefore, identification of artifacts from the EEG signal and handle it in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, where it combines Wavelet transform, Independent Component Analysis (ICA) with Hierarchical clustering method. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where after handling artifacts EEG signals show acceptable considering visual inspection.

  • 190.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Scalable Framework for Distributed Case-based Reasoning for Big data analytics2018Inngår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 111-114Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a scalable framework for distributed case-based reasoning methodology to provide actionable knowledge based on historical big amount of data. The framework addresses several challenges, i.e., promptly analyse big data, cross-domain, use-case specific data processing, multi-source case representation, dynamic case-management, uncertainty, check the plausibility of solution after adaptation etc. through its’ five modules architectures. The architecture allows the functionalities with distributed data analytics and intended to provide solutions under different conditions, i.e. data size, velocity, variety etc.

  • 191.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals2015Inngår i: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, Vol. 211, s. 241-248Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data is difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

  • 192.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Towards Distributed k-NN similarity for Scalable Case Retrieval2018Inngår i: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings, 2018, s. 151-160Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In Big data era, the demand of processing large amount of data posing several challenges. One biggest challenge is that it is no longer possible to process the data in a single machine. Similar challenges can be assumed for case-based reasoning (CBR) approach, where the size of a case library is increasing and constructed using heterogenous data sources. To deal with the challenges of big data in CBR, a distributed CBR system can be developed, where case libraries or cases are distributed over clusters. MapReduce programming framework has the facilities of parallel processing massive amount of data through a distributed system. This paper proposes a scalable case-representation and retrieval approach using distributed k-NN similarity. The proposed approach is considered to be developed using MapReduce programming framework, where cases are distributed in many clusters.

  • 193.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Sweden.
    AUTOMATED EEG ARTIFACTS HANDLING FOR DRIVER SLEEPINESS MONITORING2016Inngår i: 2nd International Symposium on Somnolence, Vigilance, and Safety SomnoSafe2016, 2016Konferansepaper (Fagfellevurdert)
  • 194.
    Bashir, Shariq
    et al.
    Mohammad Ali Jinnah University, Islamabad, Pakistan.
    Afzal, Wasif
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Baig, Rauf
    Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
    Opinion-based entity ranking using learning to rank2016Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 38, nr 1, s. 151-163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevantentities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many rankingfeatures, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.

  • 195.
    Baumgart, S.
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Zhang, X.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Variability management in product lines of safety critical embedded systems2014Inngår i: International Conference on Embedded Systems, ICES 2014, 2014, s. 98-103Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The product line engineering approach is a promising concept to identify and manage reuse in a structured and efficient way and is even applied for the development of safety critical embedded systems. Managing the complexity of variability and addressing functional safety at the same time is challenging and is not yet solved. Variability management is an enabler to both establish traceability and making necessary information visible for safety engineers. We identify a set of requirements for such a method and evaluate existing variability management methods. We apply the most promising method to an industrial case and study its suitability for developing safety critical product family members. This study provides positive feedback on the potential of the model-based method PLUS in supporting the development of functional safety critical embedded systems in product lines. As a result of our analysis we suggest potential improvements for it.

  • 196.
    Baumgart, Stephan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Volvo Construction Equipment.
    Incorporating Functional Safety in Model-based Development of Product Lines2016Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Product lines in industry are often based on an engineer’s focus on fast and feasible product instantiation rather than a precise product line development method and process as described in literature. When considering functional safety, we need a precise model that includes evidence for the safety of each variant of the product.Functional safety standards provide guidance to develop safety critical products and require that evidence is collected to prove the safety of the product. But today’s functional safety standards do not provide guidance on how to achieve functional safety in product lines. At the same time arguments need to be collected during development so that each product configuration is safe and is fulfilling the requirements of the standards. Providing these arguments requires tracing safety-related requirements and dependencies through the development process taking the impact of variability in different development artifacts into consideration.

    In this thesis, we study the challenges of developing safety critical products in product lines. We explore industrial practices to achieve functional safety standard compliance in product lines by interviewing practitioners from different companies and by collecting the reported challenges and practices. This information helps us to identify improvement areas and we derive requirements that a product line engineering method needs to fulfill. Based on these findings we analyze variability management methods from the software product line engineering research domain to identify potential candidate solutions that can be adapted to support safety critical products. We provide an approach for capturing functional safety related characteristics in a model-based product line engineering method. We apply our method in an industrial case demonstrating the applicability.

  • 197.
    Baumgart, Stephan
    et al.
    E&E System Architecture Department, Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Functional Safety in Product Lines - A Systematic Mapping Study2016Inngår i: 42nd Euromicro Conference series on Software Engineering and Advanced Applications SEAA 2016, 2016, s. 313-322Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Software product line engineering is a widely used approach to plan and manage reuse of software. When safety critical products are developed, achieving functional safety standard compliance must be shown. The requirements stated in the functional safety standards also apply when safety critical products are developed in product lines. Managing functional safety in industrial product lines is challenging and work around solutions are applied in practice. The objective of this research is to collect and review reported research publications focusing on achieving safety in product lines and to identify gaps in todays research. We conduct a systematic mapping study of research publications reported until January 2016.We identify 39 research articles to be included in a list of primary studies and analyze how product lines are documented, which safety-related topics are covered and which evaluation method the studies apply. Generally, we find that the area of how to achieve functional safety in product lines needs more attention. Our study provides an overview on which topics have been discussed until now and which safety-related topics need more attention.

  • 198.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE ICT/SICS Västerås, Sweden.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Can STPA be used for a System-of-Systems? Experiences from an Automated Quarry Site2018Inngår i: 4th IEEE International Symposium on Systems Engineering, ISSE 2018 - Proceedings, 2018, nr 4, artikkel-id 8544433Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Automation is becoming prevalent in more and more industrial domains due to the potential benefits in cost reduction as well as the new approaches/solutions they enable. When machines are automated and utilized in system-of-systems, a thorough analysis of potential critical scenarios is necessary to derive appropriate design solutions that are safe as well. Hazard analysis methods like PHA, FTA or FMEA help to identify and follow up potential risks for the machine operators or bystanders and are well-established in the development process for safety critical machinery. However, safety certified individual machines can no way guarantee safety in the context of system-of-systems since their integration and interactions could bring forth newer hazards. Hence it is paramount to understand the application sce- narios of the system-of-systems and to apply a structured method to identify all potential hazards. In this paper, we 1) provide an overview of proposed hazard analysis methods for system-of- systems, 2) describe a case from construction equipment domain, and 3) apply the well-known System-Theoretic Process Analysis (STPA)f to our case. Our experiences during the case study and the analysis of results clearly point out certain inadequacies of STPA in the context of system-of-systems and underlines the need for the development of improved techniques for safety analysis of system-of-systems.

  • 199.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. SICS Swedish ICT, Sweden.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Defining a Method to Perform Effective Hazard Analysis for a Directed SoS Based on STPA2018Inngår i: Third Swedish Workshop on the Engineering of Systems-of-Systems 2018 SWESoS 2018, 2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    —Automating a quarry site as developed within the electric site research project at Volvo Construction Equipment is an example of a directed system-of-systems (SoS). In our case automated machines and connected smart systems are utilized to improve the work-flow at the site. We currently work on conducting hazard and safety analyses on the SoS level. Performing a hazard analysis on a SoS has been a challenge in terms of complexity and work effort. We elaborate on the suitability of methods, discuss requirements on a feasible method, and propose a tailoring of the STPA method to leverage complexity.

  • 200.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalens högskola, Akademin för innovation, design och teknik, Innovation och produktrealisering.
    Punnekkat, Sasikumar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. BIT-Pilani KK Birla Goa Campus, India.
    Enhancing Model-Based Engineering of Product Lines by Adding Functional Safety2015Inngår i: CEUR Workshop Proceedings, vol. 1487, 2015, s. 53-62Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Today's industrial product lines in the automotive and construction equipment domain face the challenge to show functional safety standard compliance and argue for the absence of failures for all derived product variants. The product line approaches are not su cient to support practitioners to trace safety-related characteristics through development. We aim to provide aid in creating a safety case for a certain con guration in a product line such that overall less e ort is necessary for each con guration. In this paper we 1) discuss the impact of functional safety on product line development, 2) propose a model-based approach to capture safety-related characteristics during concept phase for product lines and 3) analyze the usefulness of our proposal.

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