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Vasilakos, AthanasiosORCID iD iconorcid.org/0000-0003-1902-9877
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Publications (10 of 257) Show all publications
Chude-Okonkwo, U. K., Paul, B. S. & Vasilakos, A. (2023). Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey. IEEE Access, 11, 21210-21240
Open this publication in new window or tab >>Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 21210-21240Article, review/survey (Refereed) Published
Abstract [en]

Precision medicine (PM) is an innovative medical approach that considers differences in the individuals’ omics, medical histories, lifestyles, and environmental information in treating diseases. To fully achieve the envisaged gains of PM, various contemporary and future technologies have to be employed, among which are nanotechnology, sensor network, big data, and artificial intelligence. These technologies and other applications require a communication network that will enable them to work in tandem for the benefit of PM. Hence, communication technology serves as the nervous system of PM, without which the entire system collapses. Therefore, it is essential to explore and determine the candidate communication technology requirements that can guarantee the envisioned gains of PM. To the best of our knowledge, no work exploring how communication technology directly impacts the development and deployment of PM solutions exists. This survey paper is designed to stimulate discussions on PM from the communication engineering perspective. We introduce the fundamentals of PM and the demands in terms of quality of service that each of the enabling technologies of PM places on the communication network. We explore the information in the literature to suggest the ideal metric values of the key performance indicators for the implementation of the different components of PM. The comparative analysis of the suitability of the contemporary and future communication technologies for PM implementation is discussed. Finally, some open research challenges for the candidate communication technologies that will enable the full implementation of PM solutions are highlighted.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Precision medicine, Fourth industrial revolution (4IR) technologies, Communication technologies, 5G, 6G
National Category
Computer Engineering
Identifiers
urn:nbn:se:ltu:diva-90684 (URN)10.1109/access.2022.3175573 (DOI)000946217800001 ()2-s2.0-85130446020 (Scopus ID)
Available from: 2022-05-20 Created: 2022-05-20 Last updated: 2023-05-05Bibliographically approved
Li, C., Yang, H., Sun, Z., Yao, Q., Bao, B., Zhang, J. & Vasilakos, A. (2023). Federated Hierarchical Trust-based Interaction Scheme for Cross-domain Industrial IoT. IEEE Internet of Things Journal, 10(1), 447-457
Open this publication in new window or tab >>Federated Hierarchical Trust-based Interaction Scheme for Cross-domain Industrial IoT
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2023 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 1, p. 447-457Article in journal (Refereed) Published
Abstract [en]

The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This paper proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Industrial internet of things, cross-domain, blockchain, federated learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:ltu:diva-92660 (URN)10.1109/jiot.2022.3200854 (DOI)000911309300033 ()2-s2.0-85137604679 (Scopus ID)
Note

Funder: Beijing Natural Science Foundation (grant no. 4202050); NSFC project (grant no. 61871056 and 62122015); Fund of SKL of IPOC (grant no. IPOC2020A004 and (IPOC2021ZT04)

Available from: 2022-08-26 Created: 2022-08-26 Last updated: 2023-05-05Bibliographically approved
Yang, H., Yuan, J., Li, C., Zhao, G., Sun, Z., Yao, Q., . . . Zhang, J. (2022). BrainIoT: Brain-Like Productive Services Provisioning with Federated Learning in Industrial IoT. IEEE Internet of Things Journal, 9(3), 2014-2024
Open this publication in new window or tab >>BrainIoT: Brain-Like Productive Services Provisioning with Federated Learning in Industrial IoT
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2022 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 9, no 3, p. 2014-2024Article in journal (Refereed) Published
Abstract [en]

The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Artificial intelligence (AI), brain, federated learning (FL), Industrial Internet of Things (IIoT), services provisioning
National Category
Communication Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-85659 (URN)10.1109/JIOT.2021.3089334 (DOI)000747462100034 ()2-s2.0-85110529159 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-03-07 (joosat);

Funder: NSFC (61871056); Beijing Natural Science Foundation (4202050); Key Laboratory Fund (6142104190412); SKL of IPOC (BUPT) (IPOC2020A004) and (IPOC2018A001).

Available from: 2021-06-18 Created: 2021-06-18 Last updated: 2022-07-04Bibliographically approved
Zhao, C., Tang, Y., Sun, Q. & Vasilakos, A. V. (2022). Deep Direct Visual Odometry. IEEE transactions on intelligent transportation systems (Print), 23(7), 7733-7742
Open this publication in new window or tab >>Deep Direct Visual Odometry
2022 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 7, p. 7733-7742Article in journal (Refereed) Published
Abstract [en]

Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Visual odometry, direct methods, pose estimation, deep learning, unsupervised learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-84133 (URN)10.1109/TITS.2021.3071886 (DOI)000732877700001 ()2-s2.0-85104622568 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-08-19 (sofila);

Finansiär:  National Natural Science Foundation of China (61988101); International (Regional) Cooperation and Exchange Project (61720106008); Program of Shanghai Academic Research Leader (20XD1401300); Programme of Introducing Talents of Discipline to Universities (B17017)

Available from: 2021-05-05 Created: 2021-05-05 Last updated: 2022-08-19Bibliographically approved
Tan, S., Wang, Y. & Vasilakos, A. V. (2022). Distributed Population Dynamics for Searching Generalized Nash Equilibria of Population Games With Graphical Strategy Interactions. IEEE Transactions on Systems, Man & Cybernetics. Systems, 52(5), 3263-3272
Open this publication in new window or tab >>Distributed Population Dynamics for Searching Generalized Nash Equilibria of Population Games With Graphical Strategy Interactions
2022 (English)In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 2168-2232, Vol. 52, no 5, p. 3263-3272Article in journal (Refereed) Published
Abstract [en]

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Dynamical communication networks, Nash equilibrium seeking, population dynamics, potential game
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-83315 (URN)10.1109/TSMC.2021.3062827 (DOI)000732359600001 ()2-s2.0-85102689376 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-04-20 (johcin);

Finansiär: National Natural Science Foundation of China (61873088, 61503130)

A correction is available for this publication, please see: Tan, S., Wang, Y., Vasilakos, A. V. Erratum to: Distributed Population Dynamics for Searching Generalized Nash Equilibria of Population Games With Graphical Strategy Interactions. IEEE Transactions on Systems, Man, and Cybernetics: Systems 53, 12 (2023). DOI: 10.1109/TSMC.2021.3069059

Available from: 2021-03-19 Created: 2021-03-19 Last updated: 2024-02-09Bibliographically approved
Cai, S., Wang, D., Wang, H., Lyu, Y., Xu, G., Zheng, X. & Vasilakos, A. V. (2022). DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling. IEEE Journal on Selected Areas in Communications, 40(2), 611-625
Open this publication in new window or tab >>DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
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2022 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 40, no 2, p. 611-625Article in journal (Refereed) Published
Abstract [en]

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Edge computing, deep learning training, dynamic scheduling, convolutional neural network
National Category
Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-87417 (URN)10.1109/jsac.2021.3118419 (DOI)000742724700013 ()2-s2.0-85119617658 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-03-07 (joosat);

Funder: National Key R&D Program of China (2019YFB2101700, 2018YFB0804402); National Science Foundation of China (U1736115); Key Research and Development Project of Sichuan Province (21SYSX0082)

Available from: 2021-10-07 Created: 2021-10-07 Last updated: 2022-07-04Bibliographically approved
Elghamrawy, S. M., Hassanien, A. E. & Vasilakos, A. V. (2022). Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning. International journal of imaging systems and technology (Print), 32(2), 614-628
Open this publication in new window or tab >>Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning
2022 (English)In: International journal of imaging systems and technology (Print), ISSN 0899-9457, E-ISSN 1098-1098, Vol. 32, no 2, p. 614-628Article in journal (Refereed) Published
Abstract [en]

The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. 

Place, publisher, year, edition, pages
John Wiley & Sons, 2022
Keywords
artificial intelligence, classification algorithms, deep learning, evolutionary computation, genetic algorithms, hybrid intelligent systems, medical diagnostic, predictive model
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-86627 (URN)10.1002/ima.22644 (DOI)000684784700001 ()34518740 (PubMedID)2-s2.0-85112359521 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-03-10 (hanlid)

Available from: 2021-08-16 Created: 2021-08-16 Last updated: 2022-03-10Bibliographically approved
Liu, X., Deng, R. H., Miao, Y. & Vasilakos, A. V. (2022). Guest Editorial: 5G-enabled Intelligent Application for Distributed Industrial Internet-of-Thing System. IEEE Transactions on Industrial Informatics, 18(4), 2807-2810
Open this publication in new window or tab >>Guest Editorial: 5G-enabled Intelligent Application for Distributed Industrial Internet-of-Thing System
2022 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 18, no 4, p. 2807-2810Article in journal, Editorial material (Other academic) Published
Abstract [en]

As a novel network infrastructure that realizes the interconnection of humans, machines, and things, the 5G-based or blockchain-based applications have been widely deployed in the Industrial Internet of Things (IIoT). However, there are still many challenges to be solved, such as poor scalability, low efficiency, and privacy leakages. In the special issue, we present eight advanced solutions in data analysis, secure communication, authentication, access control, and data deduplication.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
5G-enabled applications, Internet of Things, data analysis, secure communication, authentication, access control, data deduplication
National Category
Communication Systems
Identifiers
urn:nbn:se:ltu:diva-87783 (URN)10.1109/tii.2021.3123971 (DOI)000739636900066 ()2-s2.0-85118626980 (Scopus ID)
Available from: 2021-11-04 Created: 2021-11-04 Last updated: 2023-09-06Bibliographically approved
Xiong, H., Choo, K.-K. R. & Vasilakos, A. (2022). Revocable Identity-Based Access Control for Big Data with Verifiable Outsourced Computing. IEEE Transactions on Big Data, 8(1), 1-13
Open this publication in new window or tab >>Revocable Identity-Based Access Control for Big Data with Verifiable Outsourced Computing
2022 (English)In: IEEE Transactions on Big Data, E-ISSN 2332-7790, Vol. 8, no 1, p. 1-13Article in journal (Refereed) Published
Abstract [en]

To be able to leverage big data to achieve enhanced strategic insight, process optimization and make informed decision, we need to be an efficient access control mechanism for ensuring end-to-end security of such information asset. Signcryption is one of several promising techniques to simultaneously achieve big data confidentiality and authenticity. However, signcryption suffers from the limitation of not being able to revoke users from a large-scale system efficiently. We put forward, in this paper, the first identity-based (ID-based) signcryption scheme with efficient revocation as well as the feature to outsource unsigncryption to enable secure big data communications between data collectors and data analytical system(s). Our scheme is designed to achieve end-to-end confidentiality, authentication, non-repudiation, and integrity simultaneously, while providing scalable revocation functionality such that the overhead demanded by the private key generator (PKG) in the key-update phase only increases logarithmically based on the cardiality of users. Although in our scheme the majority of the unsigncryption tasks are outsourced to an untrusted cloud server, this approach does not affect the security of the proposed scheme. We then prove the security of our scheme, as well as demonstrating its utility using simulations

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-64471 (URN)10.1109/TBDATA.2017.2697448 (DOI)000742723200001 ()2-s2.0-85123676739 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-02-09 (johcin)

Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2022-03-30Bibliographically approved
Pradeep, P., Krishnamoorthy, S. & Vasilakos, A. (2021). A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware. Pervasive and Mobile Computing, 72, Article ID 101342.
Open this publication in new window or tab >>A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware
2021 (English)In: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 72, article id 101342Article in journal (Refereed) Published
Abstract [en]

The Internet of Things is envisioned to provide connectivity and communication between various devices all over the world. Based on the devices and applications involved, the nature of the network formed differs. Thus, an intelligent and holistic ecosystem needs to be created wherein clients, data sources, smart objects, and services can all co-exist and interact with each other. We present Adaptive Ubiquitous Middleware for context-aware IoT ecosystems, which considers the situational context of the applications, devices, or people and the contexts of the network formed and accordingly adapts the behavior of the ecosystem. Adaptive Ubiquitous Middleware is a multi-agent, multi-communication protocol-facilitated middleware that acts as an integration point for applications to access relevant context, share it with other applications, and have relevant services made available via a multi-communication protocol bridge. We also present an optimal service allocation model for a single service class that utilizes available computing resources and achieves a minimum average response time. The system implementation has been evaluated with two use cases to demonstrate its applicability, effectiveness, and generality. The evaluation of optimal service allocation demonstrates the service response is much faster in the proposed model.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Context-aware computing, Context-aware services, Intelligent systems, Internet of Things, Middleware, Queuing model
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-82925 (URN)10.1016/j.pmcj.2021.101342 (DOI)000632639400005 ()2-s2.0-85101330433 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-02-18 (alebob);

Finansiär: Amrita Vishwa Vidyapeetham

Available from: 2021-02-12 Created: 2021-02-12 Last updated: 2021-04-14Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-1902-9877

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