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Vasilakos, AthanasiosORCID iD iconorcid.org/0000-0003-1902-9877
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Publications (10 of 226) Show all publications
Zhang, J., Yang, X., Xie, N., Zhang, X., Vasilakos, A. V. & Li, W. (2020). An online auction mechanism for time-varying multidimensional resource allocation in clouds. Future generations computer systems, 111, 27-38
Open this publication in new window or tab >>An online auction mechanism for time-varying multidimensional resource allocation in clouds
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2020 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 111, p. 27-38Article in journal (Refereed) Published
Abstract [en]

Multidimensional resource allocation is a hot topic in cloud computing, but current cloud platforms support only fixed resource allocation, that is, the user resource requirements are consistent throughout the usage period, which may cause a waste of resources and reduce the revenue of resource providers. Therefore, time-varying multidimensional resource allocation and the corresponding pricing mechanism represent a new challenge in cloud computing. We address the problem of online time-varying multidimensional resource allocation and pricing in clouds. Specifically, (1) we propose a novel integer programming model for the time-varying multidimensional resource allocation problem and (2) we design a truthful online auction mechanism for resource allocation in a competitive environment. For the resource allocation algorithm, we propose a waiting period strategy and dominant-resource-based strategy to improve the social welfare and resource utilization. Simultaneously, a payment pricing algorithm based on critical value theory is proposed. Finally, we prove that the mechanism is truthful and individual rationality. Compared with existing research, our approach is characterized by high social welfare, high resource utilization and short execution time.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Cloud computing, Time-varying resource allocation, Online truthful mechanism, Dominant resources, Critical value
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78717 (URN)10.1016/j.future.2020.04.029 (DOI)2-s2.0-85083788352 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-29 (alebob)

Available from: 2020-04-29 Created: 2020-04-29 Last updated: 2020-05-05Bibliographically approved
Yi,, J.-H., Xing, L.-N. -., Wang, G.-G., Dong, J., Vasilakos, A., Alavi, A. & Wang, L. (2020). Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences, 509, 470-487
Open this publication in new window or tab >>Behavior of crossover operators in NSGA-III for large-scale optimization problems
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2020 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 509, p. 470-487Article in journal (Refereed) Published
Abstract [en]

Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Electroencephalography, Large-scale optimization, Big data optimization, Evolutionary multi-objective optimization, NSGA-III, Crossover operator, Performance analysis
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71519 (URN)10.1016/j.ins.2018.10.005 (DOI)000494883700030 ()2-s2.0-85055637462 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-10-09 (johcin)

Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2019-11-21Bibliographically approved
Kumar, N., Vasilakos, A., Choo, K.-K. R. & Yang, L. T. (2020). Energy Management for Cyber-Physical Cloud Systems. Future generations computer systems, 105, 754-756
Open this publication in new window or tab >>Energy Management for Cyber-Physical Cloud Systems
2020 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 105, p. 754-756Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78040 (URN)10.1016/j.future.2019.05.072 (DOI)
Available from: 2020-03-13 Created: 2020-03-13 Last updated: 2020-03-13Bibliographically approved
Sun, G., Zhou, R., Sun, J., Yu, H. & Vasilakos, A. V. (2020). Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization. IEEE Internet of Things Journal
Open this publication in new window or tab >>Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization
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2020 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662Article in journal (Refereed) Epub ahead of print
Abstract [en]

The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features. 1) Energy-aware routing (EAR): By considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost. 2) Adaptive delayed shutdown (ADS): The delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, EAR-ADS can achieve dual energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. Simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Energy efficient, Service function chain, Network function virtualization, Dynamic deployment
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78559 (URN)10.1109/JIOT.2020.2970995 (DOI)
Available from: 2020-04-17 Created: 2020-04-17 Last updated: 2020-04-17
Chude-Okonkwo, U. A. K., Maharaj, B. T., Vasilakos, A. & Malekian, R. (2020). Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery. IEEE Transactions on Nanobioscience, 19(2), 270-284
Open this publication in new window or tab >>Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery
2020 (English)In: IEEE Transactions on Nanobioscience, ISSN 1536-1241, E-ISSN 1558-2639, Vol. 19, no 2, p. 270-284Article in journal (Refereed) Published
Abstract [en]

Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system’s bioequivalence measure.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Molecular communication, information theory, mutual information, bioequivalence
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78732 (URN)10.1109/TNB.2020.2968567 (DOI)000528540500014 ()31985433 (PubMedID)2-s2.0-85083417784 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-30 (alebob)

Available from: 2020-04-30 Created: 2020-04-30 Last updated: 2020-05-14Bibliographically approved
Wazid, M., Das, A. K., Bhat K, V. & Vasilakos, A. (2020). LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment. Journal of Network and Computer Applications, 150, Article ID 102496.
Open this publication in new window or tab >>LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment
2020 (English)In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 150, article id 102496Article in journal (Refereed) Published
Abstract [en]

Internet of Things (IoT) becomes a new era of the Internet, which consists of several connected physical smart objects (i.e., sensing devices) through the Internet. IoT has different types of applications, such as smart home, wearable devices, smart connected vehicles, industries, and smart cities. Therefore, IoT based applications become the essential parts of our day-to-day life. In a cloud-based IoT environment, cloud platform is used to store the data accessed from the IoT sensors. Such an environment is greatly scalable and it supports real-time event processing which is very important in several scenarios (i.e., IoT sensors based surveillance and monitoring). Since some applications in cloud-based IoT are very critical, the information collected and sent by IoT sensors must not be leaked during the communication. To accord with this, we design a new lightweight authentication mechanism in cloud-based IoT environment, called LAM-CIoT. By using LAM-CIoT, an authenticated user can access the data of IoT sensors remotely. LAM-CIoT applies efficient “one-way cryptographic hash functions” along with “bitwise XOR operations”. In addition, fuzzy extractor mechanism is also employed at the user's end for local biometric verification. LAM-CIoT is methodically analyzed for its security part through the formal security using the broadly-accepted “Real-Or-Random (ROR)” model, formal security verification using the widely-used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool as well as the informal security analysis. The performance analysis shows that LAM-CIoT offers better security, and low communication and computation overheads as compared to the closely related authentication schemes. Finally, LAM-CIoT is evaluated using the NS2 network simulator for the measurement of network performance parameters that envisions the impact of LAM-CIoT on the network performance of LAM-CIoT and other schemes.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Internet of Things (IoT), Cloud computing, Authentication, Key agreement, Security, AVISPA simulation
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76943 (URN)10.1016/j.jnca.2019.102496 (DOI)000512219200008 ()2-s2.0-85075628619 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-12-09 (johcin)

Available from: 2019-11-28 Created: 2019-11-28 Last updated: 2020-03-24Bibliographically approved
Chen, J., Zhou, J., Cao, Z., Vasilakos, A., Dong, X. & Choo, K.-K. R. (2020). Lightweight Privacy-preserving Training and Evaluation for Discretized Neural Networks. IEEE Internet of Things Journal, 7(4), 2663-2678
Open this publication in new window or tab >>Lightweight Privacy-preserving Training and Evaluation for Discretized Neural Networks
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2020 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 4, p. 2663-2678Article in journal (Refereed) Published
Abstract [en]

Machine learning, particularly the neural network, is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this paper, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed. Firstly, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain including multivariate polynomial, nonlinear activation function, gradient function and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed, which can also be extended to convolutional neural network. Finally, we give the formal security proofs for dataset privacy, model training privacy and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in discretized neural network to demonstrate the high efficiency and accuracy of our proposed LPTE.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Discretized neural networks, privacy-preserving, secure outsourced computation, efficiency, Neural networks, Training, Computational modeling, Data privacy, Public key
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76111 (URN)10.1109/JIOT.2019.2942165 (DOI)2-s2.0-85083744687 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-23 (alebob)

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2020-05-05Bibliographically approved
Vasudev, H., Das, D. & Vasilakos, A. V. (2020). Secure message propagation protocols for IoVs communication components. Computers & electrical engineering, 82, Article ID 106555.
Open this publication in new window or tab >>Secure message propagation protocols for IoVs communication components
2020 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 82, article id 106555Article in journal (Refereed) Published
Abstract [en]

With the development of Internet of Vehicles (IoVs), the smart transportation field has achieved a lot of attention by providing a wide variety of benefits, such as enhanced road safety, reduced traffic congestion, traveler safety, and less pollution. It made the concept of ‘intelligence on wheels’ into a real one. However, the highly dynamic nature of vehicles and insecure channels are the significant challenges in an IoV environment. In the existing researches, secure and lightweight protocols for the IoVs communication components are missing. In this paper, we design secure and lightweight communication protocols for different components of IoVs, such as V2V (Vehicle-to-Vehicle), V2P (Vehicle-to-Portable Device), V2R (Vehicle-to-Road Side Unit), V2I (Vehicle-to-Infrastructure), and V2S (Vehicle-to-Sensor). We have done in-depth security analysis to ensure the resistant power against different strong attacks. Moreover, we have implemented the protocols on a Desktop Computer and Raspberry Pi. From the results, it is observed that the proposed protocols perform well in the perspectives of communication, storage, computation, and battery consumption than other competitive protocols.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Vehicular Ad hoc networks (VANETs), Internet of Vehicles (IoVs), Security, Authentication, Communication
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78587 (URN)10.1016/j.compeleceng.2020.106555 (DOI)000521116900012 ()2-s2.0-85078707950 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-20 (alebob)

Available from: 2020-04-20 Created: 2020-04-20 Last updated: 2020-04-20Bibliographically approved
Zhang, Y.-Q., Li, X. & Vasilakos, A. (2020). Spectral Analysis of Epidemic Thresholds of Temporal Networks. IEEE Transactions on Cybernetics, 50(5), 1965-1977
Open this publication in new window or tab >>Spectral Analysis of Epidemic Thresholds of Temporal Networks
2020 (English)In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, Vol. 50, no 5, p. 1965-1977Article in journal (Refereed) Published
Abstract [en]

Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time. We discuss the impacts of statistical characteristics, e.g., bursts and duration heterogeneity, as well as time-reversed characteristic on epidemic thresholds. We confirm the tightness of the proposed epidemic thresholds with numerical simulations on seven artificial and empirical temporal networks and show that the epidemic threshold of our theory is more precise than those of previous studies.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Bursts, discrete-time Markov chain approach, epidemic threshold, spectral radius, temporal networks, time-reversed characteristic
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-65713 (URN)10.1109/TCYB.2017.2743003 (DOI)000528622000017 ()28910782 (PubMedID)2-s2.0-85030316174 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-23 (alebob)

Available from: 2017-09-19 Created: 2017-09-19 Last updated: 2020-05-14Bibliographically approved
Gan, W., Lin, J.-W. C., Chao, H.-C., Vasilakos, A. V. & Yu, P. S. (2020). Utility-Driven Data Analytics on Uncertain Data. IEEE Systems Journal
Open this publication in new window or tab >>Utility-Driven Data Analytics on Uncertain Data
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2020 (English)In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234Article in journal (Refereed) Epub ahead of print
Abstract [en]

Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Data analytics, Internet of Things (IoT), manufacturing data, uncertainty, utility
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78557 (URN)10.1109/JSYST.2020.2979279 (DOI)
Available from: 2020-04-17 Created: 2020-04-17 Last updated: 2020-04-17
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ORCID iD: ORCID iD iconorcid.org/0000-0003-1902-9877

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