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A Hybrid CT-DEWCA-Based Energy-Efficient Routing Protocol for Data and Storage Nodes in Underwater Acoustic Sensor Networks

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IEEE Access 2025年 13卷 91392-91408页

作者： Imran Khan Muhammad Rauf Atif Jamil Department of Electronics Engineering Dawood University of Engineering and Technology Karachi Pakistan Department of Computer System Engineering Dawood University of Engineering and Technology Karachi Pakistan

Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission enhanced by hybrid model scheme with Differential Evolution, whale optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a “strategic dual transmission” approach, which employs CT for short-distance data transfer and utilizes the DEWCA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet-loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSNs performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks.

关键词： Routing Energy efficiency Routing protocols Energy consumption optimization Clustering algorithms Relays Reliability whale optimization algorithms Underwater acoustics

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Technological Innovations in Human Resource Management: Using Cloud-Edge Computing

Technological Innovations in Human Resource Management: Usin...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Laxman Narayanrao Renapure Sagar Raghunath Powar Muskan Gupta Shaik Mastan Vali Haider Alabdeli S. B. Sawant Faculty of Management Navsahyadri Group of Institutions Pune India Karmveer Hire Arts Science Commerce and education College Kolhapur India Mandsaur University India Department of MBA Malla Reddy Engineering College and Management Sciences Hyderabad India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Bharati Vidyapeeth Deemed to be University Solapur India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Utilizing cloud-edge technological advances offers substantial opportunities for resource administration, effectiveness, and financial savings. These characteristics have promoted consumer and enterprise usage of the smart integrated cloud-edge model in the Internet of Things (IoT). In integrated cloud-edge computation, among the major difficulties is human resource management (HRM). Choosing the best capabilities depending on Quality of Service (QoS) considerations is a crucial and significant problem in IoT settings as software and hardware assets in the edge context are allotted for reacting to human demands. In the sense that system effectiveness rises and reaction duration falls with appropriate resource choosing, allotment, and surveillance, HRM may be viewed as an NP issue. This work uses the whale Optimized Technique in cloud-edge computation to offer an optimal framework for the HRM issue. According to empirical outcomes, the suggested framework outperformed the additional meta-heuristic techniques in two distinct situations: shortest response duration, allocation expenses, and growing proportion of HRs allotted.

关键词： Cloud computing Technological innovation Surveillance Metaheuristics Quality of service Motors whale optimization algorithms Resource management Internet of Things Human resource management

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Dynamic Slimmable Network with Emperor Penguin Optimizer for Enhanced Water Quality Index Forecasting

Dynamic Slimmable Network with Emperor Penguin Optimizer for...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： M.Siva Ramkumar Prabhakaran S Arun M Jayant Giri Khaled Al-Qawasmi Dept of ECE SNS College of Technology India Department of Commerce Kristu jayanti College Bangalore Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Mechanical Engineering YeshwantraoChavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai TN India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The state of the water has a significant impact on human health, ecosystems, and economic activity. For efficient environmental management, water quality metrics must be tracked and predicted. Water quality is a crucial environmental health indicator since it directly affects biodiversity, ecosystem stability, and human well-being. Accurate prediction of the Index of Water Quality (WQI) and the Classification of the quality of water (WQC) is essential for effective management of water supplies, early pollution identification, and the guarantee of clean water for a range of applications. The traditional methods often lack the accuracy and flexibility required for complex water systems. Conventional techniques frequently suffer from noise and fluctuation, which causes judgments to be inaccurate. This work presents a novel method: a dynamic slimmable network with an Emperor Penguin optimizer (DSN-EPO) for forecasting the Water Quality Index (WQI), the Square Root Sage Husa Adaptive Robust Kalman Filter (SR-SHARKF) for preprocessing, and the beluga whale optimization algorithm for feature selection. The most important elements influencing the quality of the water are identified by the beluga whale algorithm, while the SR-SHARKF efficiently reduces noise and improves data accuracy. The forecasting model is then constantly optimized by the DSN-EPO by utilizing the slimmable architecture to achieve equilibrium between computational efficiency and precision. Comparing this integrated methodology to more conventional methods, predicting performance is enhanced. The proposed method performs outstandingly, with 99% of the major metrics met.

关键词： Measurement Accuracy Heuristic algorithms Noise Water quality Predictive models Water pollution whale optimization algorithms Indexes Forecasting

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A Hybrid Method for Improving SOC Prediction in Lithium-Ion Batteries by Combining EDNN with WOA

A Hybrid Method for Improving SOC Prediction in Lithium-Ion ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： V.Indumathi R.Gopalakrishnan C.Vijayakumar A.Pavithran M.Kamalesh M.Balaji Faculty of Mechatronics Engineering K S Rangasamy College of Technology Namakkal Tamil Nadu India Faculty of Electrical and Electronics Engineering K S Rangasamy College of Technology Namakkal Tamil Nadu India K S Rangasamy College of Technology Namakkal Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The safe and effective functioning of lithium-ion (Li-ion) batteries depends on accurate State of Charge (SOC) prediction. In order to increase the accuracy of SOC estimation, this study suggests a unique hybrid model that combines the whale optimization Algorithm (WOA) with Enhanced Deep Neural Networks (EDNN). Complex, non-linear connections between battery characteristics including voltage, current, temperature, and time are well captured by the EDNN. WOA ensures improved generalization and quicker convergence by optimizing the EDNN hyperparameters. Real-world battery datasets are used to validate the model, and metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to assess performance. According to the results, the EDNN-WOA hybrid model improves reliability under a range of operating situations by drastically lowering prediction errors when compared to conventional machine learning techniques. This method shows how cutting-edge deep learning and metaheuristic optimization approaches can be combined to improve the performance and longevity of Li-ion batteries and aid in the creation of intelligent battery management systems.

关键词： Lithium-ion batteries Measurement Vehicle-to-grid Accuracy Battery management systems Predictive models whale optimization algorithms Real-time systems State of charge Root mean square

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Hybrid Solar Energy System with AI-Based Predictive

Hybrid Solar Energy System with AI-Based Predictive

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： V. Nijanthan C. Shobana Bharathi Department of Electrical and Electronics Engineering Excel Engineering College Namakkal India Department of Power Electronics & Drives Excel Engineering College Namakkal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Energy Management Techniques play an important role in innovative microgrid systems primarily dependent on Sustainable Energy Sources (SES) power generation. The irregular weather conditions, low power backup, managing the combination of ESS, and ensuring the distribution generation's reliability and low power quality. Methods: To solve these limitations, this study proposes a novel hybrid optimization technique that combines the Symbiotic Search Algorithm (SSA) and whale optimization Algorithm (WOA) for managing Renewable Energy (RE) sources. The proposed system integrates hybrid wind Photovoltaic and Wind energy systems with an advanced Hybrid Energy Storage System (HESS) that includes Battery Energy Storage (BES) and SC technology in a Microgrid (MG). Renewable energy sources ensure power balance by addressing variations in load demand and fluctuations in renewable energy generation. The output testing and validation are facilitated through control parameters; the SSA-WOA-based microgrid offers an experimental framework for bright grid power quality.

关键词： Symbiosis Photovoltaic systems Renewable energy sources Wind energy Power quality Microgrids Solar energy whale optimization algorithms Batteries Wind turbines

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Research on Security Risk Constructs in Attack Detection and Defense Schemes for Smart Grid Systems

Research on Security Risk Constructs in Attack Detection and...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Zhenqi Wang Liang Wang Zhe Yu State Grid Benxi Electric Power Supply Company Benxi Liaoning China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study proposes an AI-ADP system for the Smart Grid (SG) framework to address the increasing vulnerabilities to cyberattacks and threats in the advanced digital grid system. The AI-ADP system is an Artificial Intelligence (AI)-based Attack Detection and Prevention (ADP) tool that uses a cryptography-based recommendation system to ensure data security and integrity. The system is divided into two stages: trap detection and snare prevention. The WOA-ANN (whale optimization Algorithm-Artificial Neural Network) tool is used for trap detection and classification, while SHA-512 is used for secure communication. Evaluation results show that the proposed AI-ADP system outperforms existing methods and achieves a high accuracy of 99.12% in detecting and preventing cyberattacks in the SG framework.

关键词： Accuracy Prevention and mitigation Instruments Knowledge based systems whale optimization algorithms Smart grids Cryptography Internet of Things Computer crime Recommender systems

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Low Complexity Parameter Estimation Approach for OTFS-Based ISAC System

Low Complexity Parameter Estimation Approach for OTFS-Based ...

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IEEE Conference on Wireless Communications and Networking

作者： Jianyu Zhu Jing Liang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Given that Orthogonal time frequency space (OTFS) modulation provides high robustness to Doppler shifts, its integrated sensing and communications (ISAC) has attracted more attention. In this work, we investigate the fractional delay-Doppler (DD) estimation problem for the OTFS-based ISAC system. We establish a two-dimensional (2D) correlation structure with superimposed multiple frames and propose an efficient Maximum Likelihood algorithm based on multi-frame superposition (ML-MFS) that significantly reduces computational complexity. To further improve the estimation accuracy in multi-target scenarios, we develop an estimation method based on the whale optimization algorithm, named WOA-MFS. Simulation results demonstrate that both algorithms markedly reduce complexity, with the WOA-MFS demonstrating superior estimation accuracy.

关键词： Time-frequency analysis Accuracy Parameter estimation Correlation Simulation Two-dimensional displays Estimation Integrated sensing and communication Robustness whale optimization algorithms

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Research on Noise Suppression and Signal Enhancement in Electricity Price Anomaly Detection

Research on Noise Suppression and Signal Enhancement in Elec...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Xinru Chen Sa Zhao State Grid Shanghai South Power Supply Company Shanghai China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

As the key economic index of electricity market, electricity price is directly affected by the change of supply and demand balance. However, there are often various noises in the actual electricity market data, which makes the detection of electricity price anomalies difficult. Through effective noise suppression and signal enhancement, this paper proposes a method called PRO-WOA, which combines whale optimization algorithm and wavelet transform to achieve noise suppression and signal enhancement in power price anomaly detection. Taking the actual power trading data of a province in 2022 as the research object, we conducted comparative experiments on different models. The results show that PRO-WOA performs well in forecasting accuracy and real-time performance, significantly outperforming other comparison models.

关键词： Wavelet transforms Accuracy Electricity Biological system modeling Noise reduction Predictive models Electricity supply industry Real-time systems whale optimization algorithms Anomaly detection

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Cost-Efficient Task Scheduling in Cloud-Fog Systems using Hybrid Algorithm

Cost-Efficient Task Scheduling in Cloud-Fog Systems using Hy...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Sumit Bansal Bhim Sain Singla Himanshu Aggarwal Department of CSE Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computer Science Punjabi University Neighbourhood Campus Ralla Punjab India Department of Computer Science Punjabi University Patiala Punjab India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Workflow scheduling in cloud-fog environment aims to optimize task allocation, execution time, and resource management. The complexity and dynamic nature of tasks and resources make finding an optimal workflow scheduling algorithm challenging. To address these limitations, this study introduces a Hybrid Particle Swarm optimization Algorithm-whale optimization Algorithm (HPWOA). While Particle Swarm optimization (PSO) struggles with slow convergence and high-dimensional search spaces, whale optimization Algorithm (WOA) faces difficulties in irregular search spaces and inconsistent convergence rates. By integrating PSO and WOA, HPWOA mitigates local optimization issues and enhances optimisation efficiency. The primary objective of this work is to minimise Total Execution Cost (TEC). To validate HPWOA, a comparative analyses against PSO, WOA, and HPWOA across three scientific workflows with increasing test cases has been performed. The results demonstrate that the proposed algorithm is 1.92% better than PSO and 9.60% better than WOA in terms of TEC. The paper highlights HPWOA's effectiveness in improving workflow scheduling and identifying the optimal workflow configurations for cloud-fog environments.

关键词： Costs Scheduling algorithms Heuristic algorithms Dynamic scheduling whale optimization algorithms Resource management Particle swarm optimization optimization Faces Convergence

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Interleaved Boost Converter with Optimized MPPT Approach for Renewable Energy Based Propulsion System of EV

Interleaved Boost Converter with Optimized MPPT Approach for...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Cinu S Robin J. Raji MD Yaseen N. Jothippriya Md Mujahid Irfan I. Shivasankkar Faculty of Electrical Engineering Bethlahem Institute of Engineering Karungal Department of Electrical and Electronics Engineering Bharath Institute of Higher Education and Research Chennai India Department of Electrical and Electronics Engineering Anurag University Hyderabad Telangana India Department of Electrical and Electronics Engineering Erode Sengunthar Engineering College Perundurai Erode India Department of Electrical and Electronics Engineering SR University Warangal Telangana India Department of Electrical and Electronics Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Human civilization is based on transportation, which is essential to social advancement, economic growth, and global connectivity. While fossil fuel vehicles have been the backbone of transportation for over a century, their drawbacks have become increasingly evident in terms of environmental sustainability and public health. Therefore, a sustainable and efficient way of addressing these issues is to include Renewable Energy Sources (RESs), also known as green energy sources or alternative energy sources, into the network architecture. Therefore, this paper proposes a renewable energy - based propulsion system for Electric Vehicles (EVs) using Interleaved Boost Converter (IBC) and an optimized MPPT approach. The Photovoltaic (PV) system provides power that is allowed to IBC, and it works to current sharing between stages, which reduces the thermal stress on individual components, improving reliability. Additionally, by continuously adapting to fluctuations in surroundings, an MPPT based on whale optimization Algorithm (WOA) tracks the maximum power point more precisely. After converting the incoming DC power, the 3-phase Voltage Source Inverter (VSI) provides AC to the three-phase source. A Brush Less DC (BLDC) motor is achieved by the power use of a PI controller. The simulation is implemented in MATLAB Simulink. In contrast to WOA techniques, the results indicate that the proposed system offers excellent performance and improves the overall system efficiency.

关键词： Photovoltaic systems Voltage source inverters Software packages Transportation Propulsion Electric vehicles whale optimization algorithms Public healthcare Thermal stresses Stress

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