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An Efficient and Lightweight Commitment Scheme for IoT Data Streams 19

An Efficient and Lightweight Commitment Scheme for IoT Data ...

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Plagianakos, Vassilis Spathoulas, Georgios University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Department of Information Security and Communication Technology Gjøvik Norway

ISBN: (纸本)9798350346497

Data integrity is a crucial property for offering Internet of Things (IoT) services. Usually, this is achieved by assuming a high level of trust in the service provider, a practice that induces negative implications. In the present paper, an efficient and lightweight commitment scheme for IoT data streams is presented. The proposed scheme combines KZG polynomial commitments and a blockchain smart contract to enable full data integrity while requiring trust only at the IoT device level. Given that the IoT device operates properly, no tampering can happen at any other point of the IoT workflow, without being noticed, while the protocol offers the option for automated settlement of cases where the operator tampers with users' data. A number of experiments have been conducted to assess the computation/gas fees requirements of the scheme and validate its real-world applicability. © 2023 IEEE.

关键词： Internet of things

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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

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IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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Houseplant leaf classification system based on deep learning algorithms

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Journal of Electrical Systems and Information technology 2024年第1期11卷 1-15页

作者： Hama, Hersh M. Abdulsamad, Taib Sh. Omer, Saman M. Department of Software and Informatics Engineering University of Raparin Raniyah Iraq College of Science and Technology University of Human Development Sulaymaniyah Iraq

Botanical experts are typically relied upon to classify houseplants since even subtle differences in characteristics such as leaves can distinguish one species from another. Therefore, an automated system for recognizing houseplant leaves with accuracy and reliability becomes a valuable asset for the identification of indoor plant species. In this paper, a houseplant leaf classification system utilizing deep learning algorithms is proposed, which has been improved to effectively classify and identify a variety of houseplant leaf types. The system uses the ResNet-50 architecture based on convolutional neural network to analyze features of the leaf images and extract relevant information for classification. In addition, this work presents a newly constructed local dataset consisting of 2500 images to classify species of houseplant leaves. The dataset includes ten types of houseplant leaves that are suitable for cultivation in various climates at home. The dataset was augmented using data augmentation algorithms to increase its size and reduce overfitting. The developed system was training and testing using a local dataset. To evaluate the improved model, comparative experiments were conducted utilizing pre-trained models (original ResNet-50 and MobileNet_v2). The improved model revealed recognition accuracy of 99% with the augmented dataset and 98.60% without the augmentation, affirming its effectiveness. The improved model could potentially be used in various fields, including horticulture, plant pathology, and environmental monitoring to identify plant species.

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

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Journal of Computer science & technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

Virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-depth research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations

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Environmental science and Pollution Research 2023年第54期30卷 115666-115682页

作者： Nguyen, Duy-Hieu Štefánik, Dušan Šedivá, Tereza Lin, Chitsan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan Slovak Hydrometeorological Institute Jeséniova 17 Bratislava833 15 Slovakia Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská Dolina F1 Bratislava842 48 Slovakia Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan

Mixing layer height (MLH) is an important meteorological parameter for air quality since it significantly affects ground-level pollution in the atmosphere. This study examined the properties of the MLH on diurnal and seasonal timescales over a 3-year period (2020–2022) using high temporal resolution measurements from eight Vaisala CL31 ceilometers situated around Slovakia. Hourly averaged MLH data was retrieved from the BL-View software using merged method. The highest daily maxima for the MLH occurred mostly in summer and spring, while the lowest values occurred predominantly during winter and autumn. The average MLH daily maximum in summer was 2229 m, and just 859 m in winter. During summer, the spatial distribution of the MLH daily maxima was more uniform compared to winter, when the air masses within the individual valleys did not mix well. Correlations between ground-level pollutant concentrations and hourly mean/daily mean MLH were analyzed. The highest correlation, R≈0.6, was found for O3. For PM10, PM2.5, and NOx, the anticorrelations with MLH were found with maximum in winter (R ≈ − 0.3 for hourly data and R ≈ − 0.5 for daily mean data) but no relation in summer. Lastly, the ceilometer MLH was compared to the radiosonde retrieved MLH for various cloud covers. Our analysis is based on an extensive set of empirical data, which can improve the accuracy and effectiveness of meteorological and atmospheric chemistry models. The findings can support air pollution forecasting and warning systems, providing valuable insights for policymakers and researchers. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Air quality

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Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

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Frontiers of Computer science 2024年第2期18卷 217-229页

作者： Yizheng WANG Xin ZHANG Ying JU Qing LIU Quan ZOU Yazhou ZHANG Yijie DING Ying ZHANG Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Beidahuang Industry Group General Hospital Harbin 150088China School of Informatics Xiamen UniversityXiamen 361005China Department of Anesthesiology Hospital(T.C.M)Affiliated to Southwest Medical UniversityLuzhou 646000China Software Engineering College Zhengzhou University of Light IndustryZhengzhou 450002China

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been *** developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model *** constructed three kernels in miRNA and disease space and conducted kernel fusion using *** propagation uses matrix factorization and matrix approximation to effectively reduce computation and time *** results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

关键词： human miRNA-disease association multiple kernel learning link propagation miRNA similarity disease similarity

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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