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Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network

Computer systems Science & engineering 2022年第6期41卷 1071-1082页

作者： Abolfazl Mehbodniya Surbhi Bhatia Arwa Mashat Mohanraj Elangovan Sudhakar Sengan Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyKuwait Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Information Systems College of Computing and Information TechnologyKing Abdulaziz UniversityRabighSaudi Arabia Department of Computer Science and Engineering K.S.Rangasamy College of TechnologyTiruchengode637215Tamil NaduIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor ***, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different *** path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the *** energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works.

关键词： Wireless sensor network proportional fairness multi-user routing energy consumption greedy instance fair Boltzmann distribution

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Assessing the Silent Frontlines: Exploring the Impact of DDoS Hacktivism in the Russo-Ukrainian War 40

Assessing the Silent Frontlines: Exploring the Impact of DDo...

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40th Annual Computer Security Applications Conference, ACSAC 2024

作者： Yilmaz, Yagiz Cetin, Orcun Ozturk, Omer Said Ekmekcioglu, Emre Arief, Budi Hernandez-Castro, Julio Faculty of Engineering and Natural Sciences Sabancı University Istanbul Turkey School of Computing University of Kent Canterbury United Kingdom Computer Systems Department Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9798331520885

This study assessed the impact and effectiveness of Distributed Denial of Service (DDoS) attacks during a period of about four months of the Russo-Ukrainian war, by observing the exchanges between the opposing sides. The data collection phase took place between the 28th of November 2022 and the 15th of April 2023. In total, we monitored 1,257 websites and web applications targeted in the conflict, with 633 targeted by pro-Russian and 624 by pro-Ukrainian entities. Only a small fraction (1.27%) of the targets remained unaffected, whereas 30.63% faced complete shutdowns. When considering the extent of the attacks conducted by the belligerents in the war, the attacks by pro-Russian entities showed a slightly more successful overall impact, with 36.18% of their targets were taken down, compared to 25.00% on the opposite side. Businesses demonstrated greater resilience against DDoS attacks compared to governmental and educational institutions. An in-depth analysis revealed significant differences in target categories, despite both sides primarily targeting businesses. Our findings regarding the usage of DDoS protection services among the 1,257 analysed targets showed that only 13.37% used such services. Among these minority of users, 70.24% had protection from the beginning of our analysis, while 29.76% adopted it only after experiencing attacks. We also looked into the use of geolocation-based access policies on websites targeted by pro-Ukrainian entities. Our findings indicated that most of these websites do not implement geolocation-based access restrictions. To an extent, such restrictions could have been useful for preventing some unsophisticated attacks. Surprisingly, only a small percentage (4.50%) restricted access to solely Russian addresses, while a fraction (12.56%) seemed to implement adaptive access policies in response to cyberattacks. Lastly, and quite surprisingly for us, we discovered that a significant number of targets on the Russian side were using an

关键词： Websites

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A Summer Introductory Programming Course with Diversity Awareness 53

A Summer Introductory Programming Course with Diversity Awar...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Jaramillo, Isabela Ortiz Garcés, Kelly Bermejo, Jaime Torres Malik, Ali Raza Duque, Alejandro Espinal Universidad de Los Andes Department of Systems and Computing Engineering Bogotá Colombia Stanford University Department of Computer Science CA United States Universidad Del Norte Department of Education Barranquilla Colombia

ISBN: (纸本)9798350336429

Considering the need to close the gap that exists in the IT sector, universities play a fundamental role in teaching women and men how to program. The Universidad de los Andes and the CSBridge association of Stanford University joined forces to teach High School students how to code in the Colombian context during the summer vacations of 2022. This document is a retrospective report of the experience and a collection of identified areas of improvement. These areas of improvement are meant for 1) students to have a better perspective towards programming and 2) to attract a greater number of women to the IT sector. Although this experience was based in the Colombian context, these proposals can be adapted to different contexts. © 2023 IEEE.

关键词： Gender IT gap programming skills Python

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Performance Evaluation of Intrusion Detection systems on the TON_IoT Datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT Datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Options for Application of Distributed Ledger Technologies in Development and Operation of Energy Communities 7

Options for Application of Distributed Ledger Technologies i...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Svetec, Erica Herencic, Lin Hrga, Alen Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532901160

Deep integration of variable renewable energy sources in electrical power systems requires widespread use of digital technologies and novel citizen-oriented business models. The emergence of distributed ledger technologies (DLTs) has spurred the development of pilot projects and the assessment of the opportunities for the application of DLTs in the energy sector. In this paper, we examine DLTs considering their key features and applications in the energy sector, with a focus on their potential use in energy communities. As a case study, we present the pilot site in the city of KriÅ½evci in Croatia, where an energy community is being established. Based on this case, we assess the potential use cases, advantages, and possible drawbacks in the application of DLTs in comparison with the business-as-usual non-DLT solutions. © 2022 University of Split, FESB.

关键词： Blockchain

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DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery 7

DDA: Dimensionality Driven Augmentation Search for Contrasti...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Zhou, Yuning Badgery, Henry Read, Matthew Bailey, James Davey, Catherine E. Department of Biomedical Engineering The University of Melbourne Australia Department of HPB/UGI Surgery St Vincent’s Hospital Melbourne Australia Department of Surgery St Vincent’s Hospital Melbourne Australia School of Computing and Information Systems The University of Melbourne Australia

Self-supervised learning (SSL) has potential for effective representation learning in medical imaging, but the choice of data augmentation is critical and domain-specific. It remains uncertain if general augmentation policies suit surgical applications. In this work, we automate the search for suitable augmentation policies through a new method called Dimensionality Driven Augmentation Search (DDA). DDA leverages the local dimensionality of deep representations as a proxy target, and differentiably searches for suitable data augmentation policies in contrastive learning. We demonstrate the effectiveness and efficiency of DDA in navigating a large search space and successfully identifying an appropriate data augmentation policy for laparoscopic surgery. We systematically evaluate DDA across three laparoscopic image classification and segmentation tasks, where it significantly improves over existing baselines. Furthermore, DDA’s optimised set of augmentations provides insight into domain-specific dependencies when applying contrastive learning in medical applications. For example, while hue is an effective augmentation for natural images, it is not advantageous for laparoscopic images. © 2024 CC-BY 4.0, Y. Zhou, H. Badgery, M. Read, J. Bailey & C. Davey.

关键词： Laparoscopy

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Grid Frequency Control Capability of Energy Storage systems: Modeling, New Control Approach, and Real-time Validation 8

Grid Frequency Control Capability of Energy Storage Systems:...

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

作者： Oshnoei, Arman Oshnoei, Soroush Jalilpoor, Kamran Soudjani, Sadegh Blaabjerg, Frede Aalborg University Department of Energy Aalborg Denmark Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark Ghent University Department of Electromechanical Systems and Metal Engineering Ghent Belgium Newcastle University School of Computing Newcastle United Kingdom

ISBN: (纸本)9798350327007

Energy storage systems (ESSs) have proved to be efficient in frequency regulation by providing flexible charging/discharging powers. This paper presents a model predictive control (MPC) with feedback correction (FC) to provide the ESS with control signals to be efficiently involved in the frequency regulation in a power system with renewable power generation. The FD is introduced to improve the accuracy of the prediction in the MPC. An approach based on the artificial neural network (ANN) is presented for optimal design of the weighting coefficients appearing in the MPC objective function. The controller performance is compared with an MPC without feedback correction, a fuzzy-PD control, and a scheme with no support from the ESS. A comparison is also made to examine the effect of weighting coefficients tuned by the ANN with those tuned by a fuzzy intelligent method and a sine-cosine algorithm. Real-time validations are provided to demonstrate the proposed method's effectiveness. © 2023 IEEE.

关键词： Model predictive control

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Normalized Least Dependent Difference: A Method in Solving Incorrectly Identified Monotonic Linearity Relationship in Linear Regression

Normalized Least Dependent Difference: A Method in Solving I...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Yeap, Herrick Han Lin Lim, Le Ying Lim, Michelle Chee Ern Chow, Brandon Chen Hong Ong, Hui Yan Kimberly Eu, Kok Seng Yap, Kian Meng Research Centre for Human-Machine Collaboration Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Petaling Jaya47500 Malaysia School of Engineering Faculty of Innovation & Technology Taylor's University Subang Jaya47500 Malaysia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

In machine learning, the reliability of an analytical method is crucial in validating the assumption of linearity for any linear regression *** existing analytical method of proving the assumption linearity, such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC) and Kendall's Tau Correlation Coefficient (KTCC), has its limitation as it does not work in monotonic relationship *** this paper, we propose a Normalized Least Dependent Difference (NLDD) method to improve the limitation of existing linearity method in identifying monotonic relationship *** calculating the difference between each data point and its predicted value on the regression line, we can determine how much the predicted value deviates from the observed value.A consistent difference between each data point and its predicted value, represented by a relative standard deviation in the y-axis that is near to its mean, suggests that the model accurately reflects the relationship between the dependent variable and the independent *** findings show that our NLDD is effective in identifying linearity in linear relationship graph and non-linearity in monotonic relationship graph. © 2024 SPIE.

关键词： Multiple linear regression

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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AI and Machine Learning Based E - Learning System For Secondary Education 7

AI and Machine Learning Based E - Learning System For Second...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Wijayawardena, G.C.S. Subasinghe, S.G.T.S. Bismi, K.H.P. Gamage, Anjalie Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

One of the key functions directly shifted to online platforms under COVID-19 is education. The paper is about an E-learning system for secondary education in Sri Lanka. Learners and teachers can access information, resources, and tools through an E-Learning system, which is a Learning Management System that integrates a number of online activities. The main functions provided through the proposed system are chatbot, final grade prediction and weak area prediction of the students. Chatbots are becoming increasingly popular in a wide range of applications, especially in those that provide intelligence support to the user, according to recent research. So, in order to speed up the aid process, these systems are often integrated with Chatbots, which can quickly and accurately read the user's questions. This paper describes the implementation of a Chatbot prototype in the educational domain: a system for providing support to students. In the beginning, the goal was to design a special architecture and communication model that would help students get the proper answers. The final grade prediction component plays major role in the system. Because when the students are graded by their marks, they can review which areas that they have to improve and work on them. This is helpful for students as well as teachers. Weak area prediction also plays a significant role, because it can help to find out the weak areas of each subject and generate Individual Student Progress Plans to predict the students' weak subjects and the subject areas of the students. This motivates students to get higher marks easily because this part is mainly focused on weak areas of students and improve those weak areas by providing several learning activities. These are the major parts of this system to have a good E-learning system for both students and Teachers. © 2022 IEEE.

关键词： Students

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