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Ericsson, N., Åkerberg, J., Björkman, M., Lennvall, T., Larsson, S. & Pei Breivold, H. (2023). A flexible communication stack design for improved software development on industrial testbeds and simulators. Computers in industry (Print), 147, Article ID 103873.
Öppna denna publikation i ny flik eller fönster >>A flexible communication stack design for improved software development on industrial testbeds and simulators
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2023 (Engelska)Ingår i: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 147, artikel-id 103873Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In order to facilitate deterministic behavior; industrial real-time communication stacks need another design than non-real-time communication stacks typically found in e.g., Internet of Things and Cloud solutions. We propose a flexible stack design that enable code reuse between testbeds and simulators, as well as how stack layers are driven and prioritized. The design can be generalized and used for non-real-time bare-metal solutions like battery powered Internet of Things. Our approach aims at extending the use of simulation during development of industrial systems in order to find logical errors and wrong assumptions earlier in the development. Conducted and evaluated experiments show that the proposed solutions are able to extend the use of simulation during development of real-time communication software. This is achieved by reusing the same code on an industrial testbed and in a discrete event simulator. In addition, the experiments show that the stack design is generalizable and enable reuse with other non-real-time embedded systems. The contribution consists of a set of building blocks for real-time systems that enable control over the system timing when executing on a simulation host while reusing the source code from an industrial testbed. Overall, this will improve the engineering situation, with respect to code reuse, flexibility and debugging. 

Ort, förlag, år, upplaga, sidor
Elsevier B.V., 2023
Nyckelord
Customized network simulator, Discrete event simulation, Flexible stack design, Industrial communication, Software development, Embedded systems, Interactive computer systems, Internet of things, Real time systems, Software design, Code reuse, Communication stacks, Discrete-event simulations, Flexible communication, Industrial communications, Network simulators, Real-time communication, Stack designs, Testbeds
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-62090 (URN)10.1016/j.compind.2023.103873 (DOI)000953140900001 ()2-s2.0-85149292015 (Scopus ID)
Tillgänglig från: 2023-03-15 Skapad: 2023-03-15 Senast uppdaterad: 2023-04-12Bibliografiskt granskad
Fan, X., Zheng, T., Sun, S., Gidlund, M. & Åkerberg, J. (2023). Can Embedded Real-Time Linux System Effectively Support Multipath Transmission?: An Experimental Study. In: Golatowski, F Ragavan, SKV Facchinetti, T Wisniewski, L Porta, M Scanzio, S (Ed.), 2023 IEEE 19TH INTERNATIONAL CONFERENCE ON FACTORY COMMUNICATION SYSTEMS, WFCS: . Paper presented at IEEE 19th International Workshop on Factory Communication Systems (WFCS), APR 26-28, 2023, Pavia, ITALY (pp. 23-30). IEEE
Öppna denna publikation i ny flik eller fönster >>Can Embedded Real-Time Linux System Effectively Support Multipath Transmission?: An Experimental Study
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2023 (Engelska)Ingår i: 2023 IEEE 19TH INTERNATIONAL CONFERENCE ON FACTORY COMMUNICATION SYSTEMS, WFCS / [ed] Golatowski, F Ragavan, SKV Facchinetti, T Wisniewski, L Porta, M Scanzio, S, IEEE, 2023, s. 23-30Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The rise of technologies such as 6G networks, edge computing, and the Industrial Internet has led to a dramatic increase in the amount of data that needs to be transmitted over heterogeneous integrated networks. The resources of embedded devices limit the ability of the Industrial Internet to transmit data. While the multipath transmission mechanism can mitigate data transmission issues of low reliability and low real-time performance from the network-level perspective. As the complexity of industry applications increases, however, the phenomenon that the high-quality data transmission is subject to the influence of the underlying layer is becoming increasingly apparent. The paper aims to explores the possibility of multipath transmission protocol running on a real-time kernel from the perspective of the operating system, as there is a lack of research and reports in this area. Based on RT-Preempt, a real-time system RT-Linux suitable for the "NXP i.MX6Q" ARM integrated board has been proposed, which replaces the native Linux kernel to optimize and enhance its real-time performance. As described in the experiment part, the original standard Linux system OR-Linux and the new RT-Linux are tested with single-threaded and multi-threaded load experiments, respectively. The results of the analysis show that this paper provides a way of validating the trial data and ensuring its accuracy using the lognormal distribution model, which is a statistical distribution used to model variables that are positive and skewed to the right. The RT-Linux scheme has better real-time performance and is more stable than the OR-Linux scheme after real-time processing, showing the viability of the scheme.

Ort, förlag, år, upplaga, sidor
IEEE, 2023
Serie
IEEE International Workshop on Factory Communication Systems, ISSN 2835-8511
Nyckelord
multipath transmission, real-time Linux, embedded system, lognormal distribution, system transplantation
Nationell ämneskategori
Annan teknik
Identifikatorer
urn:nbn:se:mdh:diva-65165 (URN)10.1109/WFCS57264.2023.10144118 (DOI)001012871100004 ()2-s2.0-85162671997 (Scopus ID)978-1-6654-6432-1 (ISBN)
Konferens
IEEE 19th International Workshop on Factory Communication Systems (WFCS), APR 26-28, 2023, Pavia, ITALY
Tillgänglig från: 2023-12-21 Skapad: 2023-12-21 Senast uppdaterad: 2023-12-21Bibliografiskt granskad
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2023). Machine Learning-Based Coarse Frequency Bands Classification For Cognitive Radio Applications. In: Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol., ECTI-CON: . Paper presented at 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023. Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Machine Learning-Based Coarse Frequency Bands Classification For Cognitive Radio Applications
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2023 (Engelska)Ingår i: Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol., ECTI-CON, Institute of Electrical and Electronics Engineers Inc. , 2023Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper is focused on multiple supervised machine learning algorithms' performance evaluation in terms of classification accuracy and speed for the blind frequency bands classification into three occupancy classes: white, gray, and black spaces for potential implementation in cognitive radio application. Training and validation data sets consisting of 180 000 samples, including 60 000 samples per class, have been collected in the controlled experiment. Data samples have been generated using a hardware signal generator and recorded on the receiver's front end as the time-domain complex signals. Gray space data samples contain one, two, or three signals modulated into 2FSK, BPSK, or QPSK with symbol rates 10, 100, or 1000 kSymbol/s. White space data samples contain no own generated signals. Black space data samples contain two signals with the symbol rate of 22.5 MSymbol/s and offset +14 MHz and -14 MHz from the central frequency occupying the entire observation band. Training and validation of twenty supervised machine learning algorithms have been performed offline in the Matlab Classification Learner application using the collected data set. Fine decision trees have demonstrated the highest classification accuracy of 87.8 %, the observed classification speed of 630000 Objects/s is also higher than the required 2000 Objects/s. Medium decision trees and ensemble boosted trees have demonstrated 87.5 % and 87.7 % accuracy and classification speeds of 950000 and 230000 Objects/s respectively. Therefore, ensemble boosted trees, and fine and medium decision trees have been selected for the deployment on the target radio application in the scope of future work.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2023
Nyckelord
cognitive radio, decision trees, machine learning, vacant frequency channels, Classification (of information), Learning algorithms, Signal receivers, Classification accuracy, Data sample, Data set, Frequency channels, Machine learning algorithms, Machine-learning, Radio applications, Space data, Supervised machine learning, Vacant frequency channel
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:mdh:diva-63918 (URN)10.1109/ECTI-CON58255.2023.10153155 (DOI)2-s2.0-85164912117 (Scopus ID)9798350310467 (ISBN)
Konferens
2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023
Tillgänglig från: 2023-07-26 Skapad: 2023-07-26 Senast uppdaterad: 2023-07-26Bibliografiskt granskad
Valieva, I., Voitenko, I., Björkman, M., Åkerberg, J. & Ekström, M. (2022). Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals. In: 2022 International Conference on Advanced Technologies for Communications (ATC): . Paper presented at 2022 International Conference on Advanced Technologies for Communications (ATC), 20-22 October 2022.
Öppna denna publikation i ny flik eller fönster >>Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals
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2022 (Engelska)Ingår i: 2022 International Conference on Advanced Technologies for Communications (ATC), 2022Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals, based on the classification between three symbol rate classes: 10, 100, and 1000 KSymbol/second using the scalogram images obtained from continuous wavelet transform with Morse wavelet. Pretrained deep learning AlexNet has been transfer learned to classify between symbol rate classes. Training, testing, and validation data sets have been composed of the artificial data generated using Bernoulli binary random signal generator modulated into FSK signal corrupted by additive white Gaussian noise (AWGN) noise with SNR ranging from 1 to 30 dB. Training and validation data sets have been augmented to obtain twice more extensive data set i.e 1800 scalogram images, compared to the original size of 900 samples. The average classification accuracy during validation has reached 99.7 % and during testing 100 % and 96.3 % for the data sets with SNR 25–30 dB and 20–25 dB respectively. The proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy, especially in conditions of low SNR.

Nationell ämneskategori
Telekommunikation
Identifikatorer
urn:nbn:se:mdh:diva-61136 (URN)10.1109/atc55345.2022.9943051 (DOI)2-s2.0-85142741268 (Scopus ID)
Konferens
2022 International Conference on Advanced Technologies for Communications (ATC), 20-22 October 2022
Tillgänglig från: 2022-12-07 Skapad: 2022-12-07 Senast uppdaterad: 2023-03-10Bibliografiskt granskad
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2022). Blind Vacant Frequency Channels Detection for Cognitive Radio. In: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022: . Paper presented at 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022. Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Blind Vacant Frequency Channels Detection for Cognitive Radio
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2022 (Engelska)Ingår i: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper is focused on the blind vacant frequency channels detection in 56 MHz observation band divided into 56 channels (1 MHz each) implemented on in-house developed hardware based on the AD9364 transceiver operating in automatic gain control (AGC) mode. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms widely used in the literature including energy detection, wavelets, and cyclostationary have been tested and evaluated for potential use in our target application. Primary, offline testing has been performed in the Matlab environment using the data samples captured on the target receiver's front end as a time-domain complex signal. Data samples containing one, two, or three signals generated by hardware signal generator and modulated into 2FSK, BPSK, or QPSK with symbol rate 10, 100, or 1000 kSymbol/s. The highest accuracy of 91.0 % has been observed in the offline detection for continuous wavelet transform, while energy detection has demonstrated 86.4 % accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculated for the AWGN noise sample and the sample containing BPSK and 2FSK modulated signals. Energy detection and discrete wavelet transform have been implemented on our target hardware and tested in the office environment in conditions that could be approximated by AWGN channel. Test sequences containing one or two signals have been generated by the signal generator and received and processed by our target radio node. Discrete wavelet transform has demonstrated 85.73 % and energy detection 85.25 % accuracy in real-time testing. © 2022 IEEE.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2022
Nyckelord
cognitive radio, energy detection, vacant frequency channels, wavelet transform
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mdh:diva-58205 (URN)10.1109/ICEIC54506.2022.9748704 (DOI)000942023400111 ()2-s2.0-85128807098 (Scopus ID)9781665409346 (ISBN)
Konferens
2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Tillgänglig från: 2022-05-11 Skapad: 2022-05-11 Senast uppdaterad: 2023-03-22Bibliografiskt granskad
Lavassani, M., Åkerberg, J. & Björkman, M. (2022). Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic. In: : . Paper presented at 21st IEEE International Symposium on Network Computing and Applications, NCA 2022, Virtual, Online, 14 December 2022 through 16 December 2022 (pp. 89-95).
Öppna denna publikation i ny flik eller fönster >>Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic
2022 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.

Nationell ämneskategori
Kommunikationssystem Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-62049 (URN)10.1109/NCA57778.2022.10013583 (DOI)2-s2.0-85147334168 (Scopus ID)9798350397307 (ISBN)
Konferens
21st IEEE International Symposium on Network Computing and Applications, NCA 2022, Virtual, Online, 14 December 2022 through 16 December 2022
Tillgänglig från: 2023-03-10 Skapad: 2023-03-10 Senast uppdaterad: 2023-04-12Bibliografiskt granskad
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2022). Machine Learning-Based Frequency Bands Classification for Efficient Frequency Hopping Spread Spectrum Applications. In: Proceedings - IEEE Military Communications Conference MILCOM: . Paper presented at 2022 IEEE Military Communications Conference, MILCOM 2022, Rockville, 28 November 2022 through 2 December 2022 (pp. 72-77). Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Machine Learning-Based Frequency Bands Classification for Efficient Frequency Hopping Spread Spectrum Applications
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2022 (Engelska)Ingår i: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 2022, s. 72-77Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper is focused on the performance evaluation of nine supervised machine learning algorithms in terms of classification accuracy applied to perform two radio scene analysis tasks: 1. blind binary frequency band occupancy classification: vacant or occupied; 2. interference type classification: sine wave interference, or modulated signal or additive white Gaussian noise (AWGN) for the frequency hopping spread spectrum cognitive radio application. Twenty-nine features derived from the time-, frequency-domain and RSSI, have been used as classification inputs to the evaluated machine learning classifiers. Classifiers training and validation have been performed offline in Matlab Classification Learner and Neural Networks applications using four data sets, generated in the controlled experiment, covering both classification tasks in AWGN and mixed channel propagation conditions (AWGN and Rician fading). Data samples have been generated using a hardware signal generator and recorded on the target application receivers' front end as the time-domain complex signals. The highest classification accuracy of 98.71 % has been demonstrated by Feed Forward Neural Network (FFNN) for the binary occupancy classification in K-fold validation for the mixed data set containing both AWGN and Rician fading channel samples. For the interference type classification, FFNN has demonstrated classification accuracy of 99.82 % for K-fold validation and 99.71 % for hold-out validation. FFNN has been concluded as an acceptable algorithm for further adaptation and embedded deployment on our target radio application for both binary classification between occupied or vacant frequency bands and interference type classification. 

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2022
Nyckelord
decision trees, frequency hopping spread spectrum, neural networks, supervised machine learning, vacant frequency bands
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mdh:diva-61925 (URN)10.1109/MILCOM55135.2022.10017912 (DOI)000968304600013 ()2-s2.0-85147333248 (Scopus ID)9781665485340 (ISBN)
Konferens
2022 IEEE Military Communications Conference, MILCOM 2022, Rockville, 28 November 2022 through 2 December 2022
Tillgänglig från: 2023-02-15 Skapad: 2023-02-15 Senast uppdaterad: 2023-05-17Bibliografiskt granskad
Lavassani, M., Åkerberg, J. & Björkman, M. (2022). Modeling and Profiling of Aggregated Industrial Network Traffic. Applied Sciences, 12(2), Article ID 667.
Öppna denna publikation i ny flik eller fönster >>Modeling and Profiling of Aggregated Industrial Network Traffic
2022 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 12, nr 2, artikel-id 667Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new tech-nologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles. 

Ort, förlag, år, upplaga, sidor
MDPI, 2022
Nyckelord
Aggregated traffic classes, Industrial network, Traffic modeling
Nationell ämneskategori
Kommunikationssystem
Identifikatorer
urn:nbn:se:mdh:diva-57099 (URN)10.3390/app12020667 (DOI)000758834900001 ()2-s2.0-85122764100 (Scopus ID)
Tillgänglig från: 2022-02-24 Skapad: 2022-02-24 Senast uppdaterad: 2023-03-13Bibliografiskt granskad
Gore, R. N., Lisova, E., Åkerberg, J. & Björkman, M. (2022). Network Calculus Approach for Packet Delay Variation Analysis of Multi-Hop Wired Networks. Applied Sciences, 12(21), Article ID 11207.
Öppna denna publikation i ny flik eller fönster >>Network Calculus Approach for Packet Delay Variation Analysis of Multi-Hop Wired Networks
2022 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 12, nr 21, artikel-id 11207Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility of using the model parameters as a shaper of the background traffic. Thus, the analysis can be beneficial in providing QoS assurances for real-time applications.

Ort, förlag, år, upplaga, sidor
MDPI, 2022
Nyckelord
packet delay variation, jitter, QoS, packet delay, moment-generating functions, network calculus, Markovian on-off model
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mdh:diva-61059 (URN)10.3390/app122111207 (DOI)000883357900001 ()2-s2.0-85141832712 (Scopus ID)
Tillgänglig från: 2022-11-30 Skapad: 2022-11-30 Senast uppdaterad: 2023-10-06Bibliografiskt granskad
Gore, R. N., Lisova, E., Åkerberg, J. & Björkman, M. (2021). CoSiNeT: A Lightweight Clock Synchronization Algorithm for Industrial IoT. In: IEEE International Conference on Industrial Cyber-Physical Systems ICPS 2021: . Paper presented at IEEE International Conference on Industrial Cyber-Physical Systems ICPS 2021, 10 May 2021, Victoria, Canada.
Öppna denna publikation i ny flik eller fönster >>CoSiNeT: A Lightweight Clock Synchronization Algorithm for Industrial IoT
2021 (Engelska)Ingår i: IEEE International Conference on Industrial Cyber-Physical Systems ICPS 2021, 2021Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Recent advances in industrial internet of things~(IIoT) and cyber-physical systems drive Industry 4.0 and lead to advanced applications. The adequate performance of time-critical automation applications depends on a clock synchronization scheme used by control systems. Network packet delay variations adversely impact the clock synchronization performance. The impact is significant in industrial sites, where software and hardware resources heavily contribute to delay variations, and where harsh environmental conditions interfere with communication network dynamics. While existing time synchronization methods for IIoT devices, e.g., Simple Network Time Protocol~(SNTP), provide adequate synchronization in good operating conditions, their performance degrades significantly with deteriorating network conditions. To overcome this issue, we propose a scalable, software-based, lightweight clock synchronization method, called CoSiNeT, for IIoT devices that maintains precise synchronization performance in a wide range of operating conditions. We have conducted measurements in local network deployments such as home and a university campus in order to evaluate the proposed algorithm performance. The results show that CoSiNeT matches well with SNTP and state-of-the-art method in good network conditions in terms of accuracy and precision; however, it outperforms them in degrading network scenarios. In our measurements, in fair network conditions, CoSiNeT improves synchronization performance by 23% and 25% compared to SNTP and state-of-the-art method. In the case of poor network conditions, it improves performance by 43% and 26%, respectively.

Nyckelord
Clock Synchronization, Industrial Automation, Cyber-physical systems, Industrial internet of things, Wireless networks, SNTP, NTP, Round Trip Delay
Nationell ämneskategori
Teknik och teknologier Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-53972 (URN)10.1109/ICPS49255.2021.9468174 (DOI)2-s2.0-85112365768 (Scopus ID)978-1-7281-6207-2 (ISBN)
Konferens
IEEE International Conference on Industrial Cyber-Physical Systems ICPS 2021, 10 May 2021, Victoria, Canada
Projekt
Future Industrial Networks
Tillgänglig från: 2021-05-28 Skapad: 2021-05-28 Senast uppdaterad: 2023-10-06Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-7159-7508

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