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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

computers, Materials & Continua 2022年第1期70卷 801-816页

作者： J.Jean Justus M.Thirunavukkarasan K.Dhayalini G.Visalaxi Adel Khelifi Mohamed Elhoseny Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India School of Computing Science and Engineering Galgotias UniversityUttar Pradesh203201India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science&Engineering Bharath Institute of Higher Education and ResearchChennai600073India Department of Computer Science and Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates College of Computer Information Technology American University in the EmiratesDubaiUnited Arab Emirates Faculty of Computers and Information Mansoura UniversityEgypt

Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

关键词： Clustering data aggregation energy consumption cluster head selection wireless sensor networks

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Assessments of Channel Coding Performance in Data Compression and Source Coding 5th

Assessments of Channel Coding Performance in Data Compressio...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Bargavi, Manju Panwar, Rahul Shyam, Gopal Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Channel coding is a technique used to boom the performance and reliability of records transmission. It is able to be used for facts compression and source coding, that are the procedures of reducing the amount of records that desires to be despatched across a *** of channel coding performance can be performed with the aid of analyzing the performance of the records compression and source coding techniques. This can be executed thru diverse measures, such as compression ratio, supply coding charge, mistakes detection and correction, and bit blunders rate. With the intention to appropriately verify the performance of the channel coding approach, all of these measures must be considered. The aim of channel coding is to reap the highest viable fee of data compression with minimal errors detection and correction, and bit blunders rates as little as possible. This guarantees that the records is transmitted securely and efficaciously. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coding errors

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Dynamic programming based optimal trading strategy model of volatile assets 23

Dynamic programming based optimal trading strategy model of ...

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8th International Conference on Big Data and Computing, ICBDC 2023

作者： Yu, Tianqi Jin, Luo Wang, Danni Cao, Qingxin Wu, Haotian School of Science Yanshan University China School of Computer Science Wenzhou-Kean University China School of Finance and Economics Qinghai University China School of Electronic Information Engineering Beijing Jiaotong University China

ISBN: (纸本)9781450399975

Market traders frequently buy and sell volatile assets with the goal of maximizing their total returns, two typical such assets are gold and bitcoin. There are many factors that may affect the transaction process and the final return, such as commission, changes in the value of volatile assets, average value of assets over a period of time, etc., which makes it more difficult to invest and trade. In this paper, a model is put forward, which uses the past stream of daily price of volatile assets (gold and bitcoin) to analyze and determine the daily trading strategies of traders, thus maximize the returns. After crawling the data, the factors affecting the value of volatile assets is first quantified using the entropy method and TOPSIS method, which is used to determine the market trading situation on each trading day, similarly, the risk factors that may exist is also quantified, which is used to consider the stability of the trading process. Subsequently, the time series is used to predict the future transaction value of the volatile assets. Finally, dynamic programming is applied to solve the optimal trading strategy model. Multiple constraints are set according to the actual market trading situation and a heuristic genetic algorithm is used to complete the solution to ensure that the model is optimal. © 2023 ACM.

关键词： Genetic algorithms

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SewerOD: A visual sewer disease detection dataset for machine learning

SewerOD: A visual sewer disease detection dataset for machin...

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2023 International Conference on Software engineering and Machine Learning, CONF-SEML 2023

作者： Wei, Wei Li, Ce Li, Shuo Chen, Zheng Yang, Feng China University of Mining & Technology School of Mechanical Electronic & Information Engineering Department of Computer Science and Technology Xueyuan Rd Haidian District Beijing100089 China Peking University School of Electronics Engineering and Computer Science Department of Computer Science and Technology Yiheyuan Rd Haidian District Beijing100871 China

The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://***/SewerOD. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless Sensor Networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Spectral Efficient Hybrid Beamforming Design for Full-Duplex Cell-Free Massive MIMO System

Spectral Efficient Hybrid Beamforming Design for Full-Duplex...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Yingyang Wu, Jiayuan Li, Jing Chen, Xuan Li, Qiang Wang, Li Jinan University College of Information Science and Technology Guangzhou510632 China Guangzhou University School of Electronics and Communication Engineering Guangzhou510006 China Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China

ISBN: (纸本)9798350351255

In this paper, we investigate a full-duplex (FD) cell-free massive multiple-input multiple-output (mMIMO) system. To cope with the cross-link interference, especially the interaccess point (inter-AP) interference, we formulate a sum spectral efficiency maximization problem by jointly optimizing the digital and analog beamforming at FD AP and digital precoder at the uplink user equipment. To solve it, we present a fully digital design first by utilizing successive convex approximations and majorization-minimization to obtain a closed-form solution in each iteration. After that, we exploit the Riemannian manifold to get a hybrid design, which is shown to approximate the fully digital one well. Simulation results show that the proposed design enhances the sum spectral efficiency as well as uplink performance effectively when compared to other benchmarks. © 2024 IEEE.

关键词： Beamforming

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Investigating the Advantages and Limitations of Math Modeling for Wireless Sensor Networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the blessings and obstacles of math modeling for wireless Sensor Networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. This paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless Sensor Networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling Sensor simulation

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Investigating the Role of Applied Cryptography in Digital Forensics 5th

Investigating the Role of Applied Cryptography in Digital Fo...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Nachappa, M.N. Negi, Bichitra Singh Dakhole, Dipali Khushalrao Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the position of cryptography in virtual forensic evaluation. It aims to discover the one-of-a-kind sorts of cryptographic techniques available to investigators and to talk about how they may be applied to beautify the accuracy of digital forensic work. It also considers the character of cryptographic algorithms and their relative strengths and weaknesses, as well as the numerous applications of cryptographic software and devices in digital forensics. The paper then argues that using cryptography in digital forensic analysis can toughen virtual forensic effects by introducing additional safety features, allowing encrypted facts to be reliably recovered and investigated, and presenting a further degree of warranty while proof is given. It additionally addresses the ethical concerns that must be taken into consideration by virtual forensic investigators during the usage of cryptography in their work. Eventually, it identifies numerous avenues for future studies into using cryptography and its ability applications in virtual forensics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital forensics

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A Hybrid Deep Learning Framework for Stock Price Forecasting with Sentimental Analysis 13

A Hybrid Deep Learning Framework for Stock Price Forecasting...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Banoth, Shobhan Yadala, Sucharitha Vice President-Data/Information Management Lead Engineer Citi Bank United States School of Computer Science and Engineering Vnr Vignana Jyothi Institute of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350380583

Social media electronic word-of-mouth data affects stock market confidence and trading. As a result, stock market sentiment research of comments is essential for stock market forecasting. Unfortunately, English is becoming the de facto language for sentiment analysis. This study translates writings from places where English is not the local language into English so that multilingual sentiment analysis can be performed. In order to predict stock market movements, this research combined social media's unstructured data with structured data, such as transaction records and technical indicators. Parameter determination has a significant impact on the effectiveness of forecasting models;in recent years, deep-learning(DL) and machine-learning(ML) models have developed as effective means of dealing with anticipating issues. In order to anticipate stock market indices and business stock prices using hybrid data in places where English is not the native language, this research utilized Long-Short-Term Memory(LSTM) models that utilized the geneticalgorithm(GA) to choose parameters. The created LSTMGA model outperformed the other ML-strategies with different types of data in terms of accuracy when it came to generating forecasts using hybrid multilingual sentiment data. As a result, the stock market forecasting method that combines the suggested LSTMGA model with hybrid sentiment analysis is both practical and encouraging. © 2024 IEEE.

关键词： Marketplaces

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