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Publications (10 of 41) Show all publications
Sarwatt, D. S., Lin, Y., Ding, J., Sun, Y. & Ning, H. (2024). Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions. IEEE transactions on intelligent transportation systems (Print)
Open this publication in new window or tab >>Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions
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2024 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed) Epub ahead of print
Abstract [en]

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Digital twins, Fundamental metaverse technologies, Industries, intelligent transportation system, Metaverse, metaverse integration, Training, Transportation, Virtual reality, X reality, E-learning, Information management, Integration, Intelligent systems, Intelligent vehicle highway systems, Virtual addresses, Fundamental metaverse technology, Intelligent transportation systems, Metaverses, Review of technologies, Technology application
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-25941 (URN)10.1109/TITS.2023.3347280 (DOI)2-s2.0-85182923382 (Scopus ID)
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-02-02Bibliographically approved
Wang, H., Ning, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., . . . Daneshmand, M. (2023). A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges. IEEE Internet of Things Journal, 10(16), 14671-14688
Open this publication in new window or tab >>A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges
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2023 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 16, p. 14671-14688Article in journal (Refereed) Published
Abstract [en]

In recent years, the concept of the Metaverse has attracted considerable attention. This paper provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multi-technology convergence; 2) sociality; 3) hyper-spatio-temporality. For the multi-technology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Blockchains, Companies, Convergence, Databases, Hyper-spatio-temporality, Internet of Things, Media, Metaverse, Multi-technology Convergence, Sociality, Blockchain, Social aspects, Virtual reality, Block-chain, Medium, Metaverses, Spatio temporalities, Technology convergence
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-24846 (URN)10.1109/JIOT.2023.3278329 (DOI)001045875700039 ()2-s2.0-85161040653 (Scopus ID)
Available from: 2023-06-16 Created: 2023-06-16 Last updated: 2023-09-04Bibliographically approved
Qammar, A., Naouri, A., Ding, J. & Ning, H. (2023). Blockchain-based optimized edge node selection and privacy preserved framework for federated learning. Cluster Computing
Open this publication in new window or tab >>Blockchain-based optimized edge node selection and privacy preserved framework for federated learning
2023 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Article in journal (Refereed) Epub ahead of print
Abstract [en]

Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Blockchain, Edge-node/device, Federated learning, Privacy, Security, Smart contracts, Cryptography, Data privacy, Learning systems, Block-chain, Edge nodes, Local model, Model training, Model updates, Node selection, Smart contract
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25450 (URN)10.1007/s10586-023-04145-0 (DOI)2-s2.0-85172016469 (Scopus ID)
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2023-10-10Bibliographically approved
Kebande, V. R. & Ding, J. (2023). Blockchain-Enabled Renewable Energy Traceability with a Crypto-based Arbitrage Pricing Model. In: Quwaider M., Awaysheh F.M., Jararweh Y. (Ed.), 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023: . Paper presented at 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023, Tartu, 18/9- 20/9 2023 (pp. 34-41). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Blockchain-Enabled Renewable Energy Traceability with a Crypto-based Arbitrage Pricing Model
2023 (English)In: 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023 / [ed] Quwaider M., Awaysheh F.M., Jararweh Y., Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 34-41Conference paper, Published paper (Refereed)
Abstract [en]

The need for Renewable Energy (RE) market decentralization and its rapid growth has led to new challenges related to energy traceability and pricing. While RE has undergone remarkable growth, the traditional methods of tracking renewable energy transactions lack transparency, making it difficult to ensure the authenticity of claims related to the source and quality of energy. Blockchain has been seen as a remarkable future technology capable of being integrated across many systems on the internet. This paper proposes a blockchainenabled solution to address these challenges by providing a secure and transparent traceability and pricing model for RE. The proposed approach uses blockchain technology to record and verify all energy transactions in a decentralized and tamper-proof manner. Additionally, the approach suggests the incorporation of smart contracts and Crypto-based Arbitrage techniques to automate the pricing of RE and the exploitation of a fair and efficient pricing mechanism. The paper goes the extra mile and presents an RE-based hypothetical case scenario that illustrates the implementation of the proposed model in an RE market while highlighting the benefits of using blockchain technology for energy traceability and pricing. The culminating discussions show that the blockchain-enabled RE traceability and pricing model offers a secure, transparent, and efficient pricing solution that can enhance trust in the RE market while promoting the transition to a sustainable energy future. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Blockchain, Pricing Model, Renewable Energy, Traceability, Commerce, Costs, Energy efficiency, Smart power grids, Arbitrage pricing models, Block-chain, Decentralisation, Energy, Pricing models, Rapid growth, Renewable energies, Renewable energy markets, Traceability model
National Category
Computer Sciences Energy Systems
Identifiers
urn:nbn:se:bth-25826 (URN)10.1109/FMEC59375.2023.10306021 (DOI)001103180200005 ()2-s2.0-85179518235 (Scopus ID)9798350316971 (ISBN)
Conference
8th International Conference on Fog and Mobile Edge Computing, FMEC 2023, Tartu, 18/9- 20/9 2023
Funder
Knowledge Foundation, 20190111
Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2023-12-31Bibliographically approved
Geremew, G. W. & Ding, J. (2023). Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder. Journal of Computer Networks and Communications, 2023, Article ID 1495642.
Open this publication in new window or tab >>Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder
2023 (English)In: Journal of Computer Networks and Communications, ISSN 2090-7141, E-ISSN 2090-715X, Vol. 2023, article id 1495642Article in journal (Refereed) Published
Abstract [en]

Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2023
National Category
Communication Systems Computer Sciences
Identifiers
urn:nbn:se:bth-25213 (URN)10.1155/2023/1495642 (DOI)001017639900001 ()2-s2.0-85164221079 (Scopus ID)
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-07Bibliographically approved
Li, R., Ding, J. & Ning, H. (2023). Emotion Arousal Assessment Based on Multimodal Physiological Signals for Game Users. IEEE Transactions on Affective Computing, 14(4), 2582-2594
Open this publication in new window or tab >>Emotion Arousal Assessment Based on Multimodal Physiological Signals for Game Users
2023 (English)In: IEEE Transactions on Affective Computing, E-ISSN 1949-3045, Vol. 14, no 4, p. 2582-2594Article in journal (Refereed) Published
Abstract [en]

Emotional arousal, an essential dimension of game users' experience, plays a crucial role in determining whether a game is successful. Game users' emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
BRV signal, Data collection, Electrocardiography, emotion arousal assessment, Emotion recognition, game user, Games, Labeling, physiological signal, Physiology, Training, Breath rate variability signal, Game, Generation method, Labelings, Multi-modal, Physiological signals
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24487 (URN)10.1109/TAFFC.2023.3265008 (DOI)2-s2.0-85153358278 (Scopus ID)
Available from: 2023-05-05 Created: 2023-05-05 Last updated: 2023-12-31Bibliographically approved
Jiang, Y., Jeusfeld, M. A., Ding, J. & Sandahl, E. (2023). Model-Based Cybersecurity Analysis: Extending Enterprise Modeling to Critical Infrastructure Cybersecurity. Business & Information Systems Engineering, 65(6), 643-676
Open this publication in new window or tab >>Model-Based Cybersecurity Analysis: Extending Enterprise Modeling to Critical Infrastructure Cybersecurity
2023 (English)In: Business & Information Systems Engineering, ISSN 2363-7005, E-ISSN 1867-0202, Vol. 65, no 6, p. 643-676Article in journal (Refereed) Published
Abstract [en]

Critical infrastructure (CIs) such as power gridslink a plethora of physical components from many differentvendors to the software systems that control them. Thesesystems are constantly threatened by sophisticated cyberattacks. The need to improve the cybersecurity of such CIs,through holistic system modeling and vulnerability analysis,cannot be overstated. This is challenging since a CIincorporates complex data from multiple interconnectedphysical and computation systems. Meanwhile, exploitingvulnerabilities in different information technology (IT) andoperational technology (OT) systems leads to variouscascading effects due to interconnections between systems.The paper investigates the use of a comprehensive taxonomyto model such interconnections and the implieddependencies within complex CIs, bridging the knowledgegap between IT security and OT security. The complexityof CI dependence analysis is harnessed by partitioningcomplicated dependencies into cyber and cyber-physicalfunctional dependencies. These defined functionaldependencies further support cascade modeling for vulnerabilityseverity assessment and identification of criticalcomponents in a complex system. On top of the proposedtaxonomy, the paper further suggests power-grid referencemodels that enhance the reproducibility and applicability ofthe proposed method. The methodology followed wasdesign science research (DSR) to support the designing andvalidation of the proposed artifacts. More specifically, thestructural, functional adequacy, compatibility, and coveragecharacteristics of the proposed artifacts are evaluatedthrough a three-fold validation (two case studies and expertinterviews). The first study uses two instantiated powergridmodels extracted from existing architectures andframeworks like the IEC 62351 series. The second studyinvolves a real-world municipal power grid. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Critical infrastructure, Domain-specific language, Cybersecurity, Power grids
National Category
Computer Sciences
Research subject
Telecommunication Systems; Computer Science; Software Engineering
Identifiers
urn:nbn:se:bth-24497 (URN)10.1007/s12599-023-00811-0 (DOI)000982391100001 ()2-s2.0-85158156411 (Scopus ID)
Available from: 2023-05-07 Created: 2023-05-07 Last updated: 2023-12-05Bibliographically approved
Qammar, A., Karim, A., Ning, H. & Ding, J. (2023). Securing federated learning with blockchain: a systematic literature review. Artificial Intelligence Review, 56(5), 3951-3985
Open this publication in new window or tab >>Securing federated learning with blockchain: a systematic literature review
2023 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 56, no 5, p. 3951-3985Article, review/survey (Refereed) Published
Abstract [en]

Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Federated learning, Blockchain, Security, Privacy, Blockchain-based FL, Systematic literature review
National Category
Information Systems
Identifiers
urn:nbn:se:bth-23698 (URN)10.1007/s10462-022-10271-9 (DOI)000854393600001 ()36160367 (PubMedID)2-s2.0-85138202238 (Scopus ID)
Note

open access

Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2023-06-19Bibliographically approved
Wassie, G., Ding, J. & Wondie, Y. (2023). Traffic prediction in SDN for explainable QoS using deep learning approach. Scientific Reports, 13(1), Article ID 20607.
Open this publication in new window or tab >>Traffic prediction in SDN for explainable QoS using deep learning approach
2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 20607Article in journal (Refereed) Published
Abstract [en]

The radical increase of multimedia applications such as voice over Internet protocol (VOIP), image processing, and video-based applications require better quality of service (QoS). Therefore, traffic Predicting and explaining the prediction models is essential. However, elephant flows from those applications still needs to be improved to satisfy Internet users. Elephant flows lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, deep learning models become a good alternative for real-time traffic management. This research aims to design a traffic predicting model that can identify elephant flows to prevent network congestion in advance. Thus, we are motivated to develop elephant flow prediction models and explain those models explicitly for network administrators’ use in the SDN network. H2O, Deep Autoencoder, and autoML predicting algorithms, including XGBoost, GBM and GDF, were employed to develop the proposed model. The performance of Elephant flow prediction models scored 99.97%, 99.99%, and 100% in validation accuracy of under construction error of 0.0003952, 0.001697, and 0.00000408 using XGBoost, GBM, and GDF algorithms respectively. The models were also explicitly explained using Explainable Artificial Intelligence. Accordingly, packet size and byte size attributes need much attention to detect elephant flows. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25780 (URN)10.1038/s41598-023-46471-8 (DOI)2-s2.0-85178258938 (Scopus ID)
Available from: 2023-12-15 Created: 2023-12-15 Last updated: 2023-12-15Bibliographically approved
Ding, J. & Naserinia, V. (2022). Blockchain for future renewable energy. In: Mohsen Parsa Moghaddam, Reza Zamani, Hassan Haes Alhelou, Pierluigi Siano (Ed.), Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives (pp. 129-146). Academic Press
Open this publication in new window or tab >>Blockchain for future renewable energy
2022 (English)In: Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives / [ed] Mohsen Parsa Moghaddam, Reza Zamani, Hassan Haes Alhelou, Pierluigi Siano, Academic Press, 2022, p. 129-146Chapter in book (Refereed)
Abstract [en]

To better optimize and control the renewable energy system and its integration with traditional grid systems and other energy systems, corresponding technologies are needed to meet its growing practical application requirements: decentralized management and control, support for decentralized decision-making, fine-grained and timely data sharing, maintain data and business privacy, support fast and low-cost electricity market transactions, maintain the security and reliability of system operation data, and prevent malicious cyberattacks. Blockchain is based on core technologies such as distributed ledgers, asymmetric encryption, consensus mechanisms, and smart contracts and has some excellent features such as decentralization, openness, independence, security, and anonymity. These characteristics seem to meet the technical requirements of future renewable energy systems partially. This chapter will systematically review how blockchain technology can potentially solve the challenges with decentralized solutions for future renewable energy systems and show a guideline to implement blockchain-based corresponding applications for future renewable energy. © 2022 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
Academic Press, 2022
Keywords
Blockchain, Decentralized framework, Decentralized management, Renewable energy
National Category
Energy Systems
Identifiers
urn:nbn:se:bth-23763 (URN)10.1016/B978-0-323-91698-1.00011-X (DOI)2-s2.0-85139682133 (Scopus ID)9780323916981 (ISBN)9780323985628 (ISBN)
Available from: 2022-10-21 Created: 2022-10-21 Last updated: 2023-05-10Bibliographically approved
Projects
CPS-based resilience for critical infrastructure protection [2019-05020_Vinnova]; University of Skövde
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8927-0968

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