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Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

computer Systems Science & engineering 2023年第4期45卷 497-512页

作者： S.Nithya Roopa P.Anandababu Sibi Amaran Rajesh Verma Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Computing Technologies SRM Institute of Science and TechnologyChennai603203India Department of Electrical Engineering King Khalid UniversityAbha61411Kingdom of Saudi Arabia

Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

关键词： Clustering wireless sensor networks network lifetime energy efficiency metaheuristics energy harvesting rider optimization

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A Detailed Analysis of Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector. © 2024 IEEE.

关键词： Bit error rate

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Capacity Bounds for Identification With Effective Secrecy

Capacity Bounds for Identification With Effective Secrecy

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rosenberger, Johannes Ibrahim, Abdalla Bash, Boulat A. Deppe, Christian Ferrara, Roberto Pereg, Uzi Technical University of Munich Tum School of Computation Information and Technology Germany University of Arizona Electrical and Computer Engineering Department United States Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Israel

ISBN: (纸本)9781665475549

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided. © 2023 IEEE.

关键词： Semantics

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

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The Current State of Research on Malware Written in the Rust Programming Language 11

The Current State of Research on Malware Written in the Rust...

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11th International Conference on Information Technology, ICIT 2023

作者： Praveen, Meghna Almobaideen, Wesam Rochester Institute of Technology Department of Electrical Engineering and Computing Dubai United Arab Emirates The University of Jordan Department of Computer Science Amman Jordan

ISBN: (纸本)9798350320060

Malware is conventionally written in the C/C++ programming languages. However, a recent trend has been observed where other languages are being used to write malware. One such language is the Rust programming language. In this paper, we conduct a systematic literature review to identify the current state of research on malware written in the Rust programming language. We outline the research gaps and challenges faced by researchers when studying malware written in the Rust programming language. The study found that while the industry is doing well in terms of researching and developing tools and techniques, much of it is not openly available. Moreover, there is a lack of academic research in this field, highlighting the need for academia to conduct more research to keep up with the evolving threat of malware. The study concludes that open research initiatives should be encouraged to ensure that the latest developments in malware research are accessible to all. The material in this paper is not endorsed, reviewed or approved by the Rust Foundation. © 2023 IEEE.

关键词： C++ (programming language)

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Efficient Data Loading with quantum Autoencoder 48

Efficient Data Loading with Quantum Autoencoder

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wu, Siang-Ruei Li, Chun-Tse Cheng, Hao-Chung Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Quantum Computing Center United Kingdom

ISBN: (纸本)9781728163277

Faithfully loading classical data into a quantum system is a core problem in quantum machine learning and various quantum information processing tasks. In this work, we propose an efficient quantum autoencoder architecture that can construct a quantum state approximating the unknown classical distribution with high precision and with only linear circuit depth. Simulation experiments show that our proposed method substantially outperforms state-of-the-art methods on a wide range of datasets by evaluating divergences between the loaded distributions and the target distribution, and it also enjoys a faster convergence rate and stability. Moreover, the proposed scheme can be efficiently implemented on near-term hybrid classical-quantum systems with very shallow circuit depths. © 2023 IEEE.

关键词： Machine learning

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Learning to Predict Arbitrary quantum Processes

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PRX quantum 2023年第4期4卷 040337-040337页

作者： Hsin-Yuan Huang Sitan Chen John Preskill Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences Caltech Pasadena California USA Department of Electrical Engineering and Computer Sciences UC Berkeley Berkeley California USA AWS Center for Quantum Computing Pasadena California USA

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process E, with a small average error over input states drawn from D. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 106 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

关键词： quantum computation quantum information processing quantum information theory

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Moderate Deviation Expansion for Fully quantum Tasks

Moderate Deviation Expansion for Fully Quantum Tasks

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作者： Ramakrishnan, Navneeth Tomamichel, Marco Berta, Mario Imperial College London Department of Computing LondonSW7 2BX United Kingdom National University of Singapore Centre for Quantum Technologies Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute for Quantum Information RWTH Aachen University Aachen52056 Germany

The moderate deviation regime is concerned with the finite block length trade-off between communication cost and error for information processing tasks in the asymptotic regime, where the communication cost approaches a capacity-like quantity and the error vanishes at the same time. We find exact characterisations of these trade-offs for a variety of fully quantum communication tasks, including quantum source coding, quantum state splitting, entanglement-assisted quantum channel coding, and entanglement-assisted quantum channel simulation. The main technical tool we derive is a tight relation between the partially smoothed max-information and the hypothesis testing relative entropy. This allows us to obtain the expansion of the partially smoothed max-information for i.i.d. states in the moderate deviation regime. © 1963-2012 IEEE.

关键词： quantum entanglement

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