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  • Open Access

    ARTICLE

    An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

    Mehran Tarif1, Mohammadhossein Homaei2,*, Amir Mosavi3,4,5

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1791-1820, 2025, DOI:10.32604/cmc.2025.063962 - 16 April 2025

    Abstract Underwater Wireless Sensor Networks (UWSNs) are gaining popularity because of their potential uses in oceanography, seismic activity monitoring, environmental preservation, and underwater mapping. Yet, these networks are faced with challenges such as self-interference, long propagation delays, limited bandwidth, and changing network topologies. These challenges are coped with by designing advanced routing protocols. In this work, we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks (UWF-RPL), an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network nodes. Our method extends RPL with the aid of fuzzy logic More >

  • Open Access

    ARTICLE

    Bidirectional LSTM-Based Energy Consumption Forecasting: Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

    Sheik Mohideen Shah1, Meganathan Selvamani1, Mahesh Thyluru Ramakrishna2,*, Surbhi Bhatia Khan3,4,5, Shakila Basheer6, Wajdan Al Malwi7, Mohammad Tabrez Quasim8

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

    Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

  • Open Access

    ARTICLE

    Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid Network

    Qinghai Ou1, Min Yang1, Jingcai Kong1, Yang Yang2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2429-2447, 2025, DOI:10.32604/cmc.2025.062067 - 16 April 2025

    Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >

  • Open Access

    ARTICLE

    A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization

    Medhat A. Tawfeek1,*, Ibrahim Alrashdi1, Madallah Alruwaili2, Fatma M. Talaat3,4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2773-2792, 2025, DOI:10.32604/cmc.2025.061773 - 16 April 2025

    Abstract Wireless Sensor Networks (WSNs) are one of the best technologies of the 21st century and have seen tremendous growth over the past decade. Much work has been put into its development in various aspects such as architectural attention, routing protocols, location exploration, time exploration, etc. This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments, such as balancing energy consumption, ensuring routing reliability, distributing network load, and selecting the shortest path. Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve… More >

  • Open Access

    ARTICLE

    A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

    Vijaya Krishna Akula1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Shrikant Vijayrao Sonekar4, Gopichand Ginnela5

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2449-2479, 2025, DOI:10.32604/cmc.2025.061486 - 16 April 2025

    Abstract The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are More >

  • Open Access

    ARTICLE

    Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

    Faisal Ghazi Beshaw1, Thamir Hassan Atyia2, Mohd Fadzli Mohd Salleh1, Mohamad Khairi Ishak3, Abdul Sattar Din1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

    Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

  • Open Access

    ARTICLE

    Multi-Neighborhood Enhanced Harris Hawks Optimization for Efficient Allocation of Hybrid Renewable Energy System with Cost and Emission Reduction

    Elaine Yi-Ling Wu*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1185-1214, 2025, DOI:10.32604/cmes.2025.064636 - 11 April 2025

    Abstract Hybrid renewable energy systems (HRES) offer cost-effectiveness, low-emission power solutions, and reduced dependence on fossil fuels. However, the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints. This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization (MNEHHO) algorithm to address the allocation of HRES components. The proposed approach integrates key technical parameters, including charge-discharge efficiency, storage device configurations, and renewable energy fraction. We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability. The MNEHHO algorithm employs multiple neighborhood structures… More >

  • Open Access

    ARTICLE

    Prediction and Comparative Analysis of Rooftop PV Solar Energy Efficiency Considering Indoor and Outdoor Parameters under Real Climate Conditions Factors with Machine Learning Model

    Gökhan Şahin1,*, Ihsan Levent2, Gültekin Işık2, Wilfried van Sark1, Sabir Rustemli3

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1215-1248, 2025, DOI:10.32604/cmes.2025.063193 - 11 April 2025

    Abstract This research investigates the influence of indoor and outdoor factors on photovoltaic (PV) power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency. To predict plant efficiency, nineteen variables are analyzed, consisting of nine indoor photovoltaic panel characteristics (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Maximum Voltage (Umpp), Maximum Current (Impp), Filling Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), Module Temperature) and ten environmental factors (Air Temperature, Air Humidity, Dew Point, Air Pressure, Irradiation, Irradiation Propagation, Wind Speed, Wind… More >

  • Open Access

    ARTICLE

    Wood Gasification in Catastrophes: Electricity Production from Light-Duty Vehicles

    Baxter L. M. Williams1,*, Henri Croft1, James Hunt1, Josh Viloria1, Nathan Sherman1, James Oliver1, Brody Green1, Alexey Turchin2, Juan B. García Martínez2, Joshua M. Pearce3,4, David Denkenberger1,2,*

    Energy Engineering, Vol.122, No.4, pp. 1265-1285, 2025, DOI:10.32604/ee.2025.063276 - 31 March 2025

    Abstract Following global catastrophic infrastructure loss (GCIL), traditional electricity networks would be damaged and unavailable for energy supply, necessitating alternative solutions to sustain critical services. These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use, such as decentralized electricity generation from wood gas. This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification. A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator. The vehicle successfully produced… More >

  • Open Access

    ARTICLE

    An Equivalent Fuel Consumption Minimizing Strategy for Fuel Cell Ships Considering Power Degradation

    Diju Gao, Shuai Li*

    Energy Engineering, Vol.122, No.4, pp. 1425-1442, 2025, DOI:10.32604/ee.2025.062101 - 31 March 2025

    Abstract To safeguard the ocean ecosystem, fuel cells are excellent candidates as the primary energy supply for marine vessels due to their high efficiency, low noise, and cleanliness. However, fuel cells in hybrid power systems are highly susceptible to load transients, which can severely damage fuel cells and shorten their lifespan. Therefore, the formulation of energy management strategies accounting for power degradation is crucial and urgent. In this study, an improved strategy for equivalent consumption minimization strategy (ECMS) considering power degradation is proposed. The improved energy control strategy effectively controls the energy distribution of hydrogen fuel… More > Graphic Abstract

    An Equivalent Fuel Consumption Minimizing Strategy for Fuel Cell Ships Considering Power Degradation

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