Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.073789 - 29 January 2026

Abstract Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides… More >

Wei Chen, Yang Wu^*, Tingting Pei, Jie Lin, Guojing Yuan

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

Okechukwu J. Obulezi^1,*, Hatem E. Semary², Sadia Nadir³, Chinyere P. Igbokwe⁴, Gabriel O. Orji¹, A. S. Al-Moisheer², Mohammed Elgarhy⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2991-3027, 2025, DOI:10.32604/cmes.2025.069553 - 30 September 2025

Abstract This study introduces the type-I heavy-tailed Burr XII (TIHTBXII) distribution, a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness, heavy tails, and diverse hazard behaviors. We meticulously develop the TIHTBXII’s mathematical foundations, including its probability density function (PDF), cumulative distribution function (CDF), and essential statistical properties, crucial for theoretical understanding and practical application. A comprehensive Monte Carlo simulation evaluates four parameter estimation methods: maximum likelihood (MLE), maximum product spacing (MPS), least squares (LS), and weighted least squares (WLS). The simulation results consistently show… More >

Peng Zhou^1,2, Wei Chen¹, Bingyu Cao^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5337-5360, 2025, DOI:10.32604/cmc.2025.065561 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things (IoT), are widely used in environmental monitoring, industrial control, and security surveillance. However, WSNs still face challenges such as inaccurate node clustering, low energy efficiency, and shortened network lifespan in practical deployments, which significantly limit their large-scale application. To address these issues, this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm (AC-ACO), aiming to optimize the energy utilization and system lifespan of WSNs. AC-ACO combines the path-planning capability of Ant Colony Optimization (ACO) with the dynamic characteristics of chaotic mapping and… More >

Nimisha Rajput^1,#, Amit Kumar¹, Raghavendra Pal^1,#, Nishu Gupta^2,*, Mikko Uitto², Jukka Mäkelä²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3839-3859, 2025, DOI:10.32604/cmes.2025.065903 - 30 June 2025

Abstract Wireless Sensor Networks (WSNs) play a critical role in automated border surveillance systems, where continuous monitoring is essential. However, limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time. To address this issue, this paper presents an innovative energy-efficient protocol based on deep Q-learning (DQN), specifically developed to prolong the operational lifespan of WSNs used in border surveillance. By harnessing the adaptive power of DQN, the proposed protocol dynamically adjusts node activity and communication patterns. This approach ensures optimal energy usage while maintaining high coverage, connectivity, and data accuracy. More >

Pravinkumar Bhujangrao Landge¹, Yashpal Singh¹, Hitesh Mohapatra², Seyyed Ahmad Edalatpanah^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1661-1688, 2025, DOI:10.32604/cmes.2025.065599 - 30 May 2025

Abstract Internet of things networks often suffer from early node failures and short lifespan due to energy limits. Traditional routing methods are not enough. This work proposes a new hybrid algorithm called ACOGA. It combines Ant Colony Optimization (ACO) and the Greedy Algorithm (GA). ACO finds smart paths while Greedy makes quick decisions. This improves energy use and performance. ACOGA outperforms Hybrid Energy-Efficient (HEE) and Adaptive Lossless Data Compression (ALDC) algorithms. After 500 rounds, only 5% of ACOGA’s nodes are dead, compared to 15% for HEE and 20% for ALDC. The network using ACOGA runs for More >

Vijaya Krishna Akula^1,*, 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 >

Jie Lin^*, Hongchi Shen, Tingting Pei, Yan Wu

Energy Engineering, Vol.122, No.1, pp. 331-347, 2025, DOI:10.32604/ee.2024.055611 - 27 December 2024

Abstract Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unit power generation costs. The service life of these modules directly affects these costs. Over time, the performance of PV modules gradually declines due to internal degradation and external environmental factors. This cumulative degradation impacts the overall reliability of photovoltaic power generation. This study addresses the complex degradation process of PV modules by developing a two-stage Wiener process model. This approach accounts for the distinct phases of degradation resulting from module aging and environmental influences. A power degradation model based on the More > Graphic Abstract

Gajjala Savithri^1,2, N. Raghavendra Sai^1,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2547-2569, 2024, DOI:10.32604/cmc.2024.055827 - 18 November 2024

Abstract In the world of wireless sensor networks (WSNs), optimizing performance and extending network lifetime are critical goals. In this paper, we propose a new model called DTLR-Net (Deep Temporal LSTM Regression Network) that employs long-short-term memory and is effective for long-term dependencies. Mobile sinks can move in arbitrary patterns, so the model employs long short-term memory (LSTM) networks to handle such movements. The parameters were initialized iteratively, and each node updated its position, mobility level, and other important metrics at each turn, with key measurements including active or inactive node ratio, energy consumption per cycle,… More >

Dukui Zheng¹, Jingfa Li^1,*, Bo Yu¹, Zhiqiang Huang¹, Yindi Zhang¹, Cuiwei Liu²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.011669

Abstract In the low and medium pressure urban gas pipe network, transporting the hydrogen-blended natural gas through polyethylene pipe is an important means to realize the large-scale delivery and utilization of hydrogen-blended natural gas. However, due to the characteristics of polymer material, polyethylene pipes will experience aging phenomenon, which will lead to the deterioration of performance and eventually result in brittle damage and failure. Therefore, it is of great significance to analyze and predict the lifetime of polyethylene pipe due to the aging in the hydrogen-blended natural gas environment to ensure the safe transportation. In this… More >