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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, 17(2), 985-1000
Öppna denna publikation i ny flik eller fönster >>A trustworthy and reliable multi-keyword search in blockchain-assisted cloud-edge storage
2024 (Engelska)Ingår i: Peer-to-Peer Networking and Applications, ISSN 1936-6442, E-ISSN 1936-6450, Vol. 17, nr 2, s. 985-1000Artikel i tidskrift (Refereegranskat) Published
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.

Ort, förlag, år, upplaga, sidor
Springer, 2024
Nyckelord
Trustworthy multi-keyword search, Reliable servers, Cloud-edge storage, Blockchain-based smart contracts
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-66107 (URN)10.1007/s12083-024-01635-9 (DOI)001157140600001 ()2-s2.0-85184229855 (Scopus ID)
Tillgänglig från: 2024-02-26 Skapad: 2024-02-26 Senast uppdaterad: 2024-03-28Bibliografiskt granskad
Shokrollahi, A., Persson, J. A., Malekian, R., Sarkheyli-Hägele, A. & Karlsson, F. (2024). Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches. Sensors, 24(5), Article ID 1533.
Öppna denna publikation i ny flik eller fönster >>Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches
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2024 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 5, artikel-id 1533Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Ort, förlag, år, upplaga, sidor
MDPI, 2024
Nyckelord
passive infrared sensors (PIR), smart buildings, IoT (internet of things), occupancy information, people counting, activity detection, machine learning
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mau:diva-66548 (URN)10.3390/s24051533 (DOI)001183072000001 ()38475069 (PubMedID)2-s2.0-85187481668 (Scopus ID)
Tillgänglig från: 2024-03-28 Skapad: 2024-03-28 Senast uppdaterad: 2024-05-02Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>PRIMϵ: Novel Privacy-preservation Model with Pattern Mining and Genetic Algorithm
2024 (Engelska)Ingår i: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 19, s. 571-585Artikel i tidskrift (Refereegranskat) 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) .

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2024
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-63769 (URN)10.1109/tifs.2023.3324769 (DOI)001123966000038 ()2-s2.0-85174806720 (Scopus ID)
Tillgänglig från: 2023-11-20 Skapad: 2023-11-20 Senast uppdaterad: 2024-01-08Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand
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2023 (Engelska)Ingår i: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
IEEE International Conference on Consumer Electronics-Berlin, ISSN 2166-6814, E-ISSN 2166-6822
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
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)
Konferens
2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023
Tillgänglig från: 2024-01-09 Skapad: 2024-01-09 Senast uppdaterad: 2024-01-09Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings
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2023 (Engelska)Ingår i: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234, Vol. 17, nr 4, s. 5798-5809Artikel i tidskrift (Refereegranskat) 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).

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nyckelord
Cryptography, Indexes, Encryption, Blockchains, Software algorithms, Servers, Threat modeling, Blockchain, multiple data owners (DOs), searchable encryption (SE), smart contract, verifiable
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-63095 (URN)10.1109/JSYST.2023.3309282 (DOI)001068992000001 ()2-s2.0-85171761136 (Scopus ID)
Tillgänglig från: 2023-10-11 Skapad: 2023-10-11 Senast uppdaterad: 2024-02-15Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>An Intelligent IoT-based Home Automation for Optimization of Electricity Use
2023 (Engelska)Ingår i: Przeglad Elektrotechniczny, ISSN 0033-2097, E-ISSN 2449-9544, Vol. 99, nr 9, s. 123-127Artikel i tidskrift (Refereegranskat) 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. 

Ort, förlag, år, upplaga, sidor
Wydawnictwo SIGMA-NOT, sp. z.o.o., 2023
Nyckelord
Smart home automation, Ensembled Machine learning algorithms, Microcontroller, Proximity Sensors
Nationell ämneskategori
Annan elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mau:diva-63020 (URN)10.15199/48.2023.09.23 (DOI)001058501400023 ()2-s2.0-85174916449 (Scopus ID)
Tillgänglig från: 2023-10-09 Skapad: 2023-10-09 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Dynamic Event-Triggered Adaptive Formation With Disturbance Rejection for Marine Vehicles Under Unknown Model Dynamics
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2023 (Engelska)Ingår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, nr 5, s. 5664-5676Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:mau:diva-57001 (URN)10.1109/tvt.2022.3231585 (DOI)000991849700011 ()2-s2.0-85146215359 (Scopus ID)
Tillgänglig från: 2023-01-02 Skapad: 2023-01-02 Senast uppdaterad: 2023-08-15Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning
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2023 (Engelska)Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 24, nr 1, s. 787-800Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:mau:diva-56495 (URN)10.1109/tits.2022.3217152 (DOI)000881952800001 ()2-s2.0-85141551954 (Scopus ID)
Tillgänglig från: 2022-12-07 Skapad: 2022-12-07 Senast uppdaterad: 2023-07-04Bibliografiskt granskad
Malekian, R. (2023). Effective Supervision for Enhancing Quality of Doctoral Research in Computer Science and Engineering. SN Computer Science, 4(5), Article ID 678.
Öppna denna publikation i ny flik eller fönster >>Effective Supervision for Enhancing Quality of Doctoral Research in Computer Science and Engineering
2023 (Engelska)Ingår i: SN Computer Science, E-ISSN 2661-8907, Vol. 4, nr 5, artikel-id 678Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2023
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-64241 (URN)10.1007/s42979-023-02167-4 (DOI)2-s2.0-85169893554 (Scopus ID)
Forskningsfinansiär
Malmö universitetMalmö universitet
Tillgänglig från: 2023-12-11 Skapad: 2023-12-11 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems
2023 (Engelska)Ingår i: New Technologies, Development and Application VI: Volume 2, Springer, 2023, s. 3-13Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 707
Nationell ämneskategori
Energiteknik
Identifikatorer
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)
Konferens
New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023
Tillgänglig från: 2023-12-12 Skapad: 2023-12-12 Senast uppdaterad: 2023-12-12Bibliografiskt granskad
Projekt
AVANS projekt: "Internet of Things Master's Program"; Malmö universitetInteraktion mellan människor och omgivning i Internet of Things-ekosystem: Design av uppkopplade system för energi-management i smarta byggnader för hållbarhet; Malmö universitet, Internet of Things and People (IOTAP)Pain App: Förutsäger neuropatiska smärtepisoder hos patienter med ryggmärgsskada genom bärbar EEG och maskininlärning; Malmö universitet
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-2763-8085

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