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Liu, X., Zhang, S., Huang, H. & Malekian, R. (2024). A trustworthy and reliable multi-keyword search in blockchain-assisted cloud-edge storage. Peer-to-Peer Networking and Applications
Open this publication in new window or tab >>A trustworthy and reliable multi-keyword search in blockchain-assisted cloud-edge storage
2024 (English)In: Peer-to-Peer Networking and Applications, ISSN 1936-6442, E-ISSN 1936-6450Article in journal (Refereed) Epub ahead of print
Abstract [en]

Edge computing has low transmission delay and unites more agile interconnected devices spread across geographies, which enables cloud-edge storage more suitable for distributed data sharing. This paper proposes a trustworthy and reliable multi-keyword search (TRMS) in blockchain-assisted cloud-edge storage, where data users can choose a faster search based on edge servers or a wider search based on cloud servers. To acquire trustworthy search results and find reliable servers, the blockchain-based smart contract is introduced in our scheme, which will execute the search algorithm and update the score-based trust management model. In this way, search results and trust scores will be published and recorded on the blockchain. By checking search results, data users can judge whether the returned documents are top-k documents. Based on the trust management model, we can punish the malicious behavior of search servers, while data users can choose more reliable servers based on trust scores. To improve efficiency, we design a threshold-based depth-first search algorithm. Extensive experiments are simulated on Hyperledger Fabric v2.4.1, which demonstrate our scheme (with 16 threads) can reduce the time cost of index construction by 92% and the time cost of search by 82%, approximately. Security analysis and extensive experiments can prove the security and efficiency of the proposed scheme.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Trustworthy multi-keyword search, Reliable servers, Cloud-edge storage, Blockchain-based smart contracts
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-66107 (URN)10.1007/s12083-024-01635-9 (DOI)001157140600001 ()2-s2.0-85184229855 (Scopus ID)
Available from: 2024-02-26 Created: 2024-02-26 Last updated: 2024-02-26Bibliographically approved
Madhusudhanan, S., Jose, A. C., Sahoo, J. & Malekian, R. (2024). PRIMϵ: Novel Privacy-preservation Model with Pattern Mining and Genetic Algorithm. IEEE Transactions on Information Forensics and Security, 19, 571-585
Open this publication in new window or tab >>PRIMϵ: Novel Privacy-preservation Model with Pattern Mining and Genetic Algorithm
2024 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 19, p. 571-585Article in journal (Refereed) Published
Abstract [en]

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users’ sensitive data. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover’s Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-63769 (URN)10.1109/tifs.2023.3324769 (DOI)001123966000038 ()2-s2.0-85174806720 (Scopus ID)
Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2024-01-08Bibliographically approved
Boiko, O., Shepeliev, D., Shendryk, V., Malekian, R. & Davidsson, P. (2023). A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand. In: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin): . Paper presented at 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand
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2023 (English)In: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares machine learning models for short-term heat demand forecasting in residential and multi-family buildings, evaluating model suitability, data impact on accuracy, computation time, and accuracy improvement methods. The findings are relevant for energy suppliers, researchers, and decision-makers in optimizing energy management and improving heat demand forecasting. The included models in the study are k-NN, Polynomial Regression, and LSTM with weather data, building type, and time index as input variables. Single-dimensional models (Autoregression, SARIMA, and Prophet) based on historical consumption are also studied. LSTM consistently outperforms other models in accuracy across different input variable combinations, measured using mean absolute percentage error (MAPE). The incorporation of historical consumption data improved the performance of k-NN and Polynomial Regression models. The paper also explores dataset volume impact on accuracy and compares training and prediction times. k-NN has the least prediction times, Polynomial Regression takes longer, and LSTM requires more time. All models exhibit acceptable prediction times for heat consumption. LSTM outperforms single-dimensional models in accuracy and has lower prediction times compared to AR, SARIMA, and Prophet models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Consumer Electronics-Berlin, ISSN 2166-6814, E-ISSN 2166-6822
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:mau:diva-64889 (URN)10.1109/icce-berlin58801.2023.10375622 (DOI)979-8-3503-2415-0 (ISBN)979-8-3503-2416-7 (ISBN)
Conference
2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-01-09Bibliographically approved
Liu, X., Zhang, S., Huang, H., Wang, W. & Malekian, R. (2023). A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings. IEEE Systems Journal, 17(4), 5798-5809
Open this publication in new window or tab >>A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings
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2023 (English)In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234, Vol. 17, no 4, p. 5798-5809Article in journal (Refereed) Published
Abstract [en]

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Cryptography, Indexes, Encryption, Blockchains, Software algorithms, Servers, Threat modeling, Blockchain, multiple data owners (DOs), searchable encryption (SE), smart contract, verifiable
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-63095 (URN)10.1109/JSYST.2023.3309282 (DOI)001068992000001 ()2-s2.0-85171761136 (Scopus ID)
Available from: 2023-10-11 Created: 2023-10-11 Last updated: 2024-02-15Bibliographically approved
Francis, A., Madhusudhanan, S., Jose, A. C. & Malekian, R. (2023). An Intelligent IoT-based Home Automation for Optimization of Electricity Use. Przeglad Elektrotechniczny, 99(9), 123-127
Open this publication in new window or tab >>An Intelligent IoT-based Home Automation for Optimization of Electricity Use
2023 (English)In: Przeglad Elektrotechniczny, ISSN 0033-2097, E-ISSN 2449-9544, Vol. 99, no 9, p. 123-127Article in journal (Refereed) Published
Abstract [en]

The world is gearing towards renewable energy sources, due to the numerous negative repercussions of fossil fuels. There is a need to increase the efficiency of power generation, transmission, distribution, and use. The proposed work intends to decrease household electricity use and provide an intelligent home automation solution with ensembled machine learning algorithms. It also delivers organized information about the usage of each item while automating the use of electrical appliances in a home. Experimental results show that with XGBoost and Random Forest classifiers, electricity usage can be fully automated at an accuracy of 79%, thereby improving energy utilization efficiency and improving quality of life of the user.

Abstract [pl]

Świat zmierza w kierunku odnawialnych Ĩródeá energii ze wzglĊdu na liczne negatywne reperkusje paliw kopalnych. Istnieje potrzeba zwiĊkszenia efektywnoĞci wytwarzania, przesyáu, dystrybucji i uĪytkowania energii. Proponowane prace mają na celu zmniejszenie zuĪycia energii elektrycznej w gospodarstwach domowych i zapewnienie inteligentnego rozwiązania automatyki domowej z poáączonymi algorytmami uczenia maszynowego. Dostarcza równieĪ zorganizowanych informacji na temat uĪytkowania kaĪdego elementu, jednoczeĞnie automatyzując korzystanie z urządzeĔ elektrycznych w domu. Wyniki eksperymentów pokazują, Īe dziĊki klasyfikatorom XGBoost i Random Forest zuĪycie energii elektrycznej moĪna w peáni zautomatyzowaü z dokáadnoĞcią do 79%, poprawiając w ten sposób efektywnoĞü wykorzystania energii i poprawiając jakoĞü Īycia uĪytkownika. 

Place, publisher, year, edition, pages
Wydawnictwo SIGMA-NOT, sp. z.o.o., 2023
Keywords
Smart home automation, Ensembled Machine learning algorithms, Microcontroller, Proximity Sensors
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mau:diva-63020 (URN)10.15199/48.2023.09.23 (DOI)001058501400023 ()2-s2.0-85174916449 (Scopus ID)
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-02-05Bibliographically approved
Hu, X., Zhu, G., Ma, Y., Li, Z., Malekian, R. & Sotelo, M. A. (2023). Dynamic Event-Triggered Adaptive Formation With Disturbance Rejection for Marine Vehicles Under Unknown Model Dynamics. IEEE Transactions on Vehicular Technology, 72(5), 5664-5676
Open this publication in new window or tab >>Dynamic Event-Triggered Adaptive Formation With Disturbance Rejection for Marine Vehicles Under Unknown Model Dynamics
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2023 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, no 5, p. 5664-5676Article in journal (Refereed) Published
Abstract [en]

This paper investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Control Engineering
Identifiers
urn:nbn:se:mau:diva-57001 (URN)10.1109/tvt.2022.3231585 (DOI)000991849700011 ()2-s2.0-85146215359 (Scopus ID)
Available from: 2023-01-02 Created: 2023-01-02 Last updated: 2023-08-15Bibliographically approved
Zhu, G., Ma, Y., Li, Z., Malekian, R. & Sotelo, M. (2023). Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning. IEEE transactions on intelligent transportation systems (Print), 24(1), 787-800
Open this publication in new window or tab >>Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning
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2023 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 24, no 1, p. 787-800Article in journal (Refereed) Published
Abstract [en]

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Control Engineering
Identifiers
urn:nbn:se:mau:diva-56495 (URN)10.1109/tits.2022.3217152 (DOI)000881952800001 ()2-s2.0-85141551954 (Scopus ID)
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-07-04Bibliographically approved
Malekian, R. (2023). Effective Supervision for Enhancing Quality of Doctoral Research in Computer Science and Engineering. SN Computer Science, 4(5), Article ID 678.
Open this publication in new window or tab >>Effective Supervision for Enhancing Quality of Doctoral Research in Computer Science and Engineering
2023 (English)In: SN Computer Science, E-ISSN 2661-8907, Vol. 4, no 5, article id 678Article in journal (Refereed) Published
Abstract [en]

This article reflects on effective supervision and possible guidance for enhancing quality of doctoral research in the computer science and engineering field. The aims of this study are (1) to understand supervision and the role of supervisors in the quality of doctoral research, (2) to elaborate on effective supervision in the computer science and engineering field and challenges in effective supervision, and (3) to identify key indicators for evaluating effective supervision with a view to improving the quality of doctoral research. After studying various pieces of literature and conducting interviews with experienced supervisors and doctoral students, the article concludes by describing important characteristics in effective supervision. Some of the features for effective supervision are common to other areas of research; however, in computer science and engineering and similar fields, it is important that a supervisor takes the role of a team member by giving proper advice on the reports, algorithm and mathematical modeling developed in the research, and demonstrating the ability to provide advice on complex problems with practical approaches.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-64241 (URN)10.1007/s42979-023-02167-4 (DOI)2-s2.0-85169893554 (Scopus ID)
Funder
Malmö UniversityMalmö University
Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2024-02-05Bibliographically approved
Shendryk, V., Malekian, R. & Davidsson, P. (2023). Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems. In: New Technologies, Development and Application VI: Volume 2. Paper presented at New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023 (pp. 3-13). Springer
Open this publication in new window or tab >>Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems
2023 (English)In: New Technologies, Development and Application VI: Volume 2, Springer, 2023, p. 3-13Conference paper, Published paper (Refereed)
Abstract [en]

A promising approach to improve sustainability within the energy sector is to incorporate renewable energy sources into existing energy systems. However, such hybrid energy systems have several characteristics that make developing and coordinating the challenging, e.g. due to the need to manage large amounts of heterogeneous data in a distributed and dynamic manner. This paper analyses important characteristics of hybrid heating systems, such as interoperability, scalability, and availability of energy sources. The purpose is to determine how the availability of different energy sources within a hybrid heating system affects sustainability and environmental impact, as well as how interoperability and scalability can affect the overall performance of the hybrid heating system. All these quality characteristic parameters were considered in the aspect of heterogeneous data management.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 707
National Category
Energy Engineering
Identifiers
urn:nbn:se:mau:diva-64309 (URN)10.1007/978-3-031-34721-4_1 (DOI)2-s2.0-85163358597 (Scopus ID)978-3-031-34720-7 (ISBN)978-3-031-34721-4 (ISBN)
Conference
New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2023-12-12Bibliographically approved
Saleem, H., Malekian, R. & Munir, H. (2023). Neural Network-Based Recent Research Developments in SLAM for Autonomous Ground Vehicles: A Review. IEEE Sensors Journal, 23(13), 13829-13858
Open this publication in new window or tab >>Neural Network-Based Recent Research Developments in SLAM for Autonomous Ground Vehicles: A Review
2023 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 23, no 13, p. 13829-13858Article, review/survey (Refereed) Published
Abstract [en]

The development of autonomous vehicles has prompted an interest in exploring various techniques in navigation. One such technique is simultaneous localization and mapping (SLAM), which enables a vehicle to comprehend its surroundings, build a map of the environment in real time, and locate itself within that map. Although traditional techniques have been used to perform SLAM for a long time, recent advancements have seen the incorporation of neural network techniques into various stages of the SLAM pipeline. This review article provides a focused analysis of the recent developments in neural network techniques for SLAM-based localization of autonomous ground vehicles. In contrast to the previous review studies that covered general navigation and SLAM techniques, this paper specifically addresses the unique challenges and opportunities presented by the integration of neural networks in this context. Existing review studies have highlighted the limitations of conventional visual SLAM, and this article aims to explore the potential of deep learning methods. This article discusses the functions required for localization, and several neural network-based techniques proposed by researchers to carry out such functions. First, it presents a general background of the issue, the relevant review studies that have already been done, and the adopted methodology in this review. Then, it provides a thorough review of the findings regarding localization and odometry. Finally, it presents our analysis of the findings, open research questions in the field, and a conclusion. A semisystematic approach is used to carry out the review.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Autonomous vehicle, deep learning, localization, neural network, odometry, pose, simultaneous localization and mapping (SLAM)
National Category
Robotics
Identifiers
urn:nbn:se:mau:diva-62427 (URN)10.1109/JSEN.2023.3273913 (DOI)001022960300002 ()2-s2.0-85162924023 (Scopus ID)
Available from: 2023-09-13 Created: 2023-09-13 Last updated: 2023-09-13Bibliographically approved
Projects
Internet of Things Master's Program; Malmö UniversityHuman-environment interaction in the Internet of Things ecosystems: Design of a connected energy management system in smart buildings for sustainability; Malmö University, Internet of Things and People (IOTAP)Pain App: Predicting neuropathic pain episodes in spinal cord injury patients through portable EEG and machine learning; Malmö University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2763-8085

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