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Publications (10 of 127) Show all publications
Gore, R. N., Lisova, E., Åkerberg, J. & Björkman, M. (2020). In Sync with Today's Industrial System Clocks. In: 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020: . Paper presented at 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 7 January 2020 through 11 January 2020 (pp. 785-790). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>In Sync with Today's Industrial System Clocks
2020 (English)In: 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 785-790Conference paper (Refereed)
Abstract [en]

Synchronization is essential for correct and consistent operation of automation systems. Synchronized devices accurately time-stamp the events and enable timely communication of messages over a communication network. In absence of a common time base, critical functions of automation systems cannot be carried out in a safe fashion. Unsynchronized systems may lead to malfunctions such as false alarms, wrong decisions and erroneous outcomes resulting into serious showstopper for plant operations. Despite technical advances in synchronization, industrial automation systems have lagged compared to telecommunication and financial services in utilization of latest synchronization technology. Thus, there is a need to investigate the adoption of synchronization in industrial networks, its current state and implementation problems. We carried out an extensive literature search in a structured way to study the evolution of synchronization in automation systems. We also investigated today's industrial automation systems and their network topologies to get insight into the synchronization techniques and mechanisms being used. As an outcome of study, the paper highlights the challenges related to synchronization in existing automation networks that need to be addressed in the immediate and short-term future. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2020
Keywords
Building Automation, Factory Automation, Heterogeneous communication, IEEE 1588, IEEE 802.1AS, IEEE C37.238, Industrial automation, Industrial networks, Last-mile connectivity, NTP, PTP, Secured synchronization, SNTP, Substation Automation, Synchronization, IEEE Standards, Intelligent buildings, Telecommunication services
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-47458 (URN)10.1109/COMSNETS48256.2020.9027323 (DOI)2-s2.0-85082169343 (Scopus ID)9781728131870 (ISBN)
Conference
2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 7 January 2020 through 11 January 2020
Note

Conference code: 158297; Export Date: 2 April 2020; Conference Paper

Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-04-02Bibliographically approved
Rabet, I., Fotouhi, H., Vahabi, M. & Björkman, M. (2020). Particle Filter for Handoff Prediction in SDN-based IoT Networks. In: : . Paper presented at International Conference on Embedded Wireless Systems and Networks (EWSN 2020).
Open this publication in new window or tab >>Particle Filter for Handoff Prediction in SDN-based IoT Networks
2020 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Standard implementation of RPL protocol has struggled to limit the impact of mobility on the throughput of the IoT network. Handoff process is of great importance to optimize the trade-off between the control overhead (for maintaining the network topology), and the delay, caused by nodes mobility. In this work, We have proposed a method for predicting future handoffs through fusion of RSSI value and Inertial Measurement Unit (IMU) information using particle filter, which is known for accuracy albeit it needs higher computation capacity. The provided analytical model indicates lower network interruption with the proposed method.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46720 (URN)
Conference
International Conference on Embedded Wireless Systems and Networks (EWSN 2020)
Available from: 2020-01-16 Created: 2020-01-16 Last updated: 2020-02-24Bibliographically approved
Brahneborg, D., Afzal, W., Causevic, A. & Björkman, M. (2020). Superlinear and Bandwidth Friendly Geo-replication for Store-And-Forward Systems. In: : . Paper presented at International Conference on Software Technologies. SciTePress
Open this publication in new window or tab >>Superlinear and Bandwidth Friendly Geo-replication for Store-And-Forward Systems
2020 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
SciTePress, 2020
Keywords
Store-and-forward, Replication, SMS
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47567 (URN)
Conference
International Conference on Software Technologies
Available from: 2020-04-23 Created: 2020-04-23 Last updated: 2020-04-23
Lindén, M., Kristoffersson, A. & Björkman, M. (2019). Embedded Sensor Systems for Health Plus (ESS-H+) An overview of scientific areas and interdisciplinary target. In: Abstractbok: . Paper presented at Medicinteknikdagarna 2019 (pp. 17-17).
Open this publication in new window or tab >>Embedded Sensor Systems for Health Plus (ESS-H+) An overview of scientific areas and interdisciplinary target
2019 (English)In: Abstractbok, 2019, p. 17-17Conference paper, Oral presentation with published abstract (Refereed)
Abstract [sv]

ESS-H+ will continue the work in the KKS research profile Embedded Sensor Systems for Health (ESS-H) at Mälardalen University. ESS-H has during six years provided important collaboration between researchers, industrial partners and healthcare organizations within three focus areas (Health monitoring at home, Health monitoring at work, and Infrastructure and communication).

The focus of the new research profile ESS-H+ is that monitoring of humans should be able to be performed anytime, anywhere. Our research challenges are focused to the areas of Reliable acquisition and management of physiological data, and Reliable distributed data analysis. Reliable acquisition and management of physiological data is a fundamental prerequisite for advancing anytime, anywhere health monitoring, towards enabling remote monitoring of more serious health conditions than what is safely possible today. Reliable distributed data analysis is a fundamental prerequisite for enabling large-scale deployment of anytime, anywhere health monitoring. Both research challenges are complex and require multi-disciplinary research.

The following important research goals will be addressed within ESS-H+: 

  • Reliable data acquisition, and design of suitable sensor systems for achieving this. 
  • Development of analysis and classification algorithms for physiological parameters. 
  • Achieve efficient distributed data fusion and decision support. 
  • Better utilization of the compute power of sensor nodes, and increased communication reliability: safety as well as security. 
  • Efficient integration of scientific results, from different scientific areas, to an efficient and user-friendly embedded sensor system. 

The research in ESS-H+ will include research within the scientific areas (Biomedical sensor technology, Biomedical signal processing, Intelligent decision support, and Reliable and secure data communication) but also a strong integration between these areas, our collaborating companies, and the end users. Our main challenge within ESS-H+ will be this interdisciplinary integration that aims towards fully operating systems, thud providing efficient integration of scientific results, from different scientific areas, to an efficient and user-friendly embedded sensor system.

National Category
Medical Engineering
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-45435 (URN)
Conference
Medicinteknikdagarna 2019
Funder
Knowledge Foundation
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-11Bibliographically approved
Vahabi, M., Fotouhi, H., Björkman, M. & Lindén, M. (2019). Evaluating a Remote Health Monitoring Application Powered by Bluetooth. In: 11th International Conference on e-Health e-Health'19: . Paper presented at 11th International Conference on e-Health e-Health'19, 17 Jul 2019, Porto, Portugal (pp. 67-74).
Open this publication in new window or tab >>Evaluating a Remote Health Monitoring Application Powered by Bluetooth
2019 (English)In: 11th International Conference on e-Health e-Health'19, 2019, p. 67-74-Conference paper, Published paper (Refereed)
Abstract [en]

It has become widely accepted that the Internet of Things (IoT) devices and technologies are the key enablers for many emerging applications including remote health monitoring. Various physiological sensing devices have been designed and equipped with different radio technologies. The choice of radio hardware plays an important role on the overall performance of the system since it imposes some limitations on the delivered quality of service. Hence, it is critical to properly evaluate the embedded radio technology based on the application requirements. In this paper, we perform extensive experiments on Shimmer physiological sensors that is one of the leading providers of wearable wireless sensor products powered by Bluetooth classic radio. Shimmer sensors are designed and used for monitoring various human health information such as temperature, heart rate, movement, etc. We review and investigate different scenarios in which Shimmer devices are used by medical practitioners to monitor the ECG signal and the movement of a human. This study shows that the Shimmer device can provide reliable data delivery by using a specific configuration. For instance, employing a maximum number of seven Shimmer devices attached on a body at home environment within the range of at most 5 m and with the sampling rate of 512 Hz would result in a reasonable quality of service, while varying these parameters may degrade the overall performance. Mobility of human body, noisy environment, and higher packet transmission rates are some examples that will reduce the system quality. © Copyright 2019 IADIS Press All rights reserved.

National Category
Engineering and Technology Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-45040 (URN)2-s2.0-85073169425 (Scopus ID)
Conference
11th International Conference on e-Health e-Health'19, 17 Jul 2019, Porto, Portugal
Projects
ESS-H - Embedded Sensor Systems for Health Research ProfileMobiFog: mobility management in Fog-assisted IoT networksHealth5G: Future eHealth powered by 5GFlexiHealth: flexible softwarized networks for digital healthcare
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-10-24Bibliographically approved
Lindén, M. & Björkman, M. (2019). Experience from industrial graduate (PhD) schools. In: IFMBE Proceedings: . Paper presented at World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018 (pp. 731-733). Springer Verlag (3)
Open this publication in new window or tab >>Experience from industrial graduate (PhD) schools
2019 (English)In: IFMBE Proceedings, Springer Verlag , 2019, no 3, p. 731-733Conference paper, Published paper (Refereed)
Abstract [en]

Traditionally, research education is performed within the universities, and the PhD students are working within a research group. However, technical development and also research is performed within companies, and the need to keep up with the latest findings in research and to strengthen the competence within the private business sector is increasing. At Mälardalen University, we have experience from working in several Industrial Graduate Schools. The collaboration with the companies gets intensified and deepened trough such programmes, and the university tends to keep the good contact with previous PhD students and their companies also many years after their graduation. The Graduate Schools also give the companies good insight in the university world. Presently, we are involved in two Graduate Schools, and several of the PhD projects are focusing within Biomedical Engineering. Further, one of the graduate schools is linked to the research profile Embedded Sensor Systems for Health, which is supported from the same financier. Companies are involved also in the research profile, and through these activities, the Industrial PhD students form a critical mass and can exchange both experience and knowledge with other companies and with university researchers.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Collaboration with industry, Industrial graduate school, Biomedical engineering, Embedded systems, Knowledge management, Students, Business sector, Collaboration with industries, Critical mass, Embedded sensors, Graduate schools, Research groups, Technical development, University researchers, Industrial research
National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-39978 (URN)10.1007/978-981-10-9023-3_132 (DOI)000449744300132 ()2-s2.0-85048251456 (Scopus ID)
Conference
World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2018-11-29Bibliographically approved
Vahabi, M., Fotouhi, H. & Björkman, M. (2019). FIREWORK: Fog orchestration for secure IoT networks. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 284: . Paper presented at Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13–14, 2019 (pp. 311-317).
Open this publication in new window or tab >>FIREWORK: Fog orchestration for secure IoT networks
2019 (English)In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 284, 2019, p. 311-317Conference paper, Published paper (Refereed)
Abstract [en]

Recent advances in Internet of Things (IoT) connectivity have made IoT devices prone to Cyber attacks. Moreover, vendors are eager to provide autonomous and open source device, which in turn adds more security threat to the system. In this paper, we consider network traffic attack, and provide a Fog-assisted solution, dubbed as FIRE- WORK, that reduces risk of security attacks by periodically monitor- ing network traffic, and applying traffic isolation techniques to overcome network congestion and performance degradation.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-43932 (URN)10.1007/978-3-030-21373-2_23 (DOI)2-s2.0-85067618185 (Scopus ID)9783030213732 (ISBN)
Conference
Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13–14, 2019
Projects
ESS-H - Embedded Sensor Systems for Health Research ProfileFuture factories in the CloudMobiFog: mobility management in Fog-assisted IoT networks
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-10-11Bibliographically approved
Fotouhi, H., Vahabi, M., Rabet, I., Björkman, M. & Alves, M. (2019). MobiFog: Mobility Management Framework for Fog-assisted IoT Networks. In: IEEE Global Conference on Internet of Things GCIoT'19: . Paper presented at IEEE Global Conference on Internet of Things GCIoT'19, 04 Dec 2019, Dubai, United Arab Emirates.
Open this publication in new window or tab >>MobiFog: Mobility Management Framework for Fog-assisted IoT Networks
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2019 (English)In: IEEE Global Conference on Internet of Things GCIoT'19, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Mobility is becoming a challenging issue in upcoming IoT applications, where it is crucial to employ mobile entities. Patients with sensors attached to their body in health monitoring application, AGVs in industrial monitoring and factory automation applications, cars with several sensing devices in vehicular applications are a few examples of use cases with the need for mobile nodes. In parallel, Fog computing has revolutionized network architecture, while enabling local processing of measurements, and reducing bandwidth overhead, which results in a more reliable system and real-time support. However, mobility management is a missing framework within the mobile IoT networks with Fog computing architecture. This paper provides a simple and generic seamless handoff model, dubbed as MobiFog, where it addresses the handoff mechanism with zero delay, while providing high reliability.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45945 (URN)
Conference
IEEE Global Conference on Internet of Things GCIoT'19, 04 Dec 2019, Dubai, United Arab Emirates
Projects
Future factories in the CloudMobiFog: mobility management in Fog-assisted IoT networksHealth5G: Future eHealth powered by 5GFlexiHealth: flexible softwarized networks for digital healthcare
Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2019-11-18Bibliographically approved
Valieva, I., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2019). Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio. In: Proceedings - IEEE Military Communications Conference MILCOM: . Paper presented at 2019 IEEE Military Communications Conference, MILCOM 2019, 12 November 2019 through 14 November 2019. Institute of Electrical and Electronics Engineers Inc., Article ID 9020735.
Open this publication in new window or tab >>Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
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2019 (English)In: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 2019, article id 9020735Conference paper, Published paper (Refereed)
Abstract [en]

In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classification speed and accuracy have been studied too. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
machine learning, modulation classification, signal-to-noise ratio, software defined radio, Analog circuits, Cognitive radio, Decision trees, Digital radio, Forestry, Learning algorithms, Learning systems, Military communications, Modulation, Nearest neighbor search, Radio, Radio transceivers, Signal to noise ratio, Software radio, Support vector machines, Classification accuracy, Classification features, Digital modulations, Modulation type classification, Quadrature components, Software-defined radios, Supervised machine learning, Classification (of information)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-47459 (URN)10.1109/MILCOM47813.2019.9020735 (DOI)2-s2.0-85082395256 (Scopus ID)9781728142807 (ISBN)
Conference
2019 IEEE Military Communications Conference, MILCOM 2019, 12 November 2019 through 14 November 2019
Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-04-09Bibliographically approved
Tran, H. V., Åkerberg, J., Björkman, M. & Tran, H.-V. (2019). RF energy harvesting: an analysis of wireless sensor networks for reliable communication. Wireless networks, 25(1), 185-199
Open this publication in new window or tab >>RF energy harvesting: an analysis of wireless sensor networks for reliable communication
2019 (English)In: Wireless networks, ISSN 1022-0038, E-ISSN 1572-8196, Vol. 25, no 1, p. 185-199Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider a wireless energy harvesting network consisting of one hybrid access point (HAP) having multiple antennas, and multiple sensor nodes each equipped with a single antenna. In contrast to conventional uplink wireless networks, the sensor nodes in the considered network have no embedded energy supply. They need to recharge the energy from the wireless signals broadcasted by the HAP in order to communicate. Based on the point-to-point and multipoints-to-point model, we propose two medium access control protocols, namely harvesting at the header of timeslot (HHT) and harvesting at the dedicated timeslot (HDT), in which the sensor nodes harvest energy from the HAP in the downlink, and then transform its stored packet into bit streams to send to the HAP in the uplink. Considering a deadline for each packet, the cumulative distribution functions of packet transmission time of the proposed protocols are derived for the selection combining and maximal ratio combining (MRC) techniques at the HAP. Subsequently, analytical expressions for the packet timeout probability and system reliability are obtained to analyze the performance of proposed protocols. Analytical results are validated by numerical simulations. The impacts of the system parameters, such as energy harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio, and the length of energy harvesting time on the packet timeout probability and the system reliability are extensively investigated. Our results show that the performance of the HDT protocol outperforms the one using the HHT protocol, and the HDT protocol with the MRC technique has the best performance and it can be a potential solution to enhance the reliability for wireless sensor networks.

Place, publisher, year, edition, pages
SPRINGER, 2019
Keywords
Energy harvesting, Wireless power transfer, Wireless sensor networks, Packet transmission time, Reliable communication
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-42765 (URN)10.1007/s11276-017-1546-6 (DOI)000457945500014 ()2-s2.0-85025069932 (Scopus ID)
Projects
SafeCOP - Safe Cooperating Cyber-Physical Systems using Wireless Communication
Funder
EU, Horizon 2020, 692529 Vinnova
Available from: 2019-02-22 Created: 2019-02-22 Last updated: 2019-04-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2419-2735

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