Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal^2,*, Mohammed K. Alzaylaee³, Heba G. Mohamed^4,5

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084409 - 23 July 2026

Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >

Amir Ijaz^*, Hashem Haghbayan, Abdul Malik, Ethiopia Nigussie, Juha Plosila

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083797 - 23 July 2026

Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >

Xiangyi Le¹, Deyu Lin^1,2,*, Yufei Zhao², Wang Miao³, Yong Liang Guan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083306 - 23 July 2026

Abstract The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this… More >

Mohammed Abdullah Alsuwaiket^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082979 - 23 July 2026

Abstract Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a More >

Yasemin Onal^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082593 - 23 July 2026

Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >

Mohammad Q. Al-Jamal¹, Mahmoud Al Jamal², Bashar S. Khassawneh^3,*, Ayoub Alsarhan^4,5, Amina Salhi⁶, Tahani Alsubait⁷

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079984 - 23 July 2026

Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >

Huajie Zhang^1,2, Xiaopeng Li^1,2, Hanlin Xiao^3,*, Lifeng Xing³, Wenyue Zhou^1,2

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.086505 - 12 July 2026

Abstract This article has no abstract. More >

Zewen Ge^1,*, Jihui Liu², Shuai Yuan¹, Mufan Zhuang^3,*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.082217 - 12 July 2026

Abstract Limiting warming to the Paris temperature goals requires a rapid scale-up of low-carbon energy, yet recent experience suggests that deployment is increasingly shaped by delivery constraints rather than by technology cost trends alone. This review synthesizes peer-reviewed evidence on five constraints that repeatedly slow net-zero buildouts: lengthy approval and grid-connection processes; capital-intensive investment profiles that heighten sensitivity to the cost of capital and revenue risk; bottlenecks in critical minerals, processing, and manufacturing; social contestation and local governance that translate into siting exclusions, delays, and cancellations; and modeling traditions that can underrepresent these non-marginal frictions. Accordingly,… More > Graphic Abstract

Kiran A. S.¹, Dayakar G. Devaru^1,*, Mohan N.², Bhaskaran Gopalakrishnan³

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080851 - 12 July 2026

Abstract The dairy processing industries are energy-intensive because of the high level of thermal and refrigeration processes. In this study, a large-scale dairy processing unit in South India has been analyzed thermodynamically and optimized by applying integrated energy and exergy analysis along with heat exchanger network (HEN) optimization. The operational data of one year of a plant manufacturing milk, curd, butter and ghee was analysed. Estimation of theoretical electrical and thermal energy requirements was carried out and compared with the actual plant energy consumption. The exergy analysis was used to identify the thermodynamic irreversibilities and the… More > Graphic Abstract

Miftakhul Maulidina^*, Erna Daniati

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080751 - 12 July 2026

Abstract The global energy transition toward net-zero emissions requires the massive development of renewable energy infrastructure. However, the efficiency and safety of installations such as wind turbines, solar panels, and energy storage systems are highly dependent on subsurface physical characteristics. Neglecting geological structures such as active faults or corrosive zones, risks structural failure and electrical system malfunctions. This research proposes the use of the Very Low Frequency Electromagnetic (VLF-EM) method as a fast and non-destructive geophysical screening tool. This method utilizes low-frequency signals to map variations in subsurface electrical conductivity. Furthermore, the data were processed using… More >