Kuldashbay Avazov¹, Jasur Sevinov^2,3, Komil Tashev⁴, Jamila Arzieva⁵, Tulkin Botirov⁶, Alpamis Kutlimuratov⁷, Akmalbek Abdusalomov^{2,4,8,9,10,11}, Boburjon Vafoev¹², Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087927 - 15 September 2026

Abstract Wireless sensor networks (WSNs) and Internet of Things (IoT) systems require lightweight cryptographic primitives that provide strong security under strict constraints on area, latency, and timing predictability. Ascon, selected by National Institute of Standards and Technology (NIST) as the standard for lightweight authenticated encryption, is well suited for such environments, yet application-oriented hardware implementations for WSN platforms remain underexplored. This paper presents a comprehensive evaluation of field programmable gate array (FPGA)-based Ascon architectures for secure WSN and IoT deployments, using iterative design with single permutation round and hybrid design with two-round unrolling across two FPGA… More >

Navod Neranjan Thilakarathne^1,*, Madhuka Priyashan Wedisinhage Don², Sharmi Malisha Dilshani³, Jamil Abedalrahim Jamil Alsayaydeh^4,*, Mohd Faizal Bin Yusof⁵, Rostam Affendi Bin Hamzah⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082189 - 15 September 2026

Abstract With the growing world population and demand for agricultural goods, agriculture requires innovative technologies that make the best use of resource, reduce waste, and increase productivity. So, smart agriculture, which involves the use of innovative digital technologies to enhance the quality and quantity of harvests, has come into play, superseding traditional agriculture. In recent years, the concept of the digital twin has intertwined with smart agriculture to enable precise control of entire farms, facilitating virtual replications. Overall, the digital twin enables continuous monitoring of real-time conditions in the field, providing valuable insights into crop health,… More >

Adeel Iqbal^1,#,*, Muhammad Faisal Siddiqui^2,#,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084851 - 13 August 2026

Abstract Hop-constrained packet routing is a fundamental problem in wireless sensor networks (WSNs), where latency constraints, energy limitations, and practical feasibility requirements greatly restrict routing choices. Traditional methods based on shortest path and greedy routing have low complexity but cannot adapt to dynamic network changes well, while reinforcement learning for routing has the potential to adapt to network variations but has not been well explored in the hard hop-constrained setting. The current study attempts to fill the gap by modeling hop-constrained routing as the decision-making problem in a finite-horizon setting. An integrated simulation environment is proposed… More >

Kiran Saleem¹, Upinder Kaur^2,*, Abdulrahman Mohammed Alamoudi³, Mai Alduailij⁴, Ahmad Subhi Salem Mufleh⁵, Ateeq Ur Rehman^6,*, Salil Bharany⁷

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083670 - 13 August 2026

Abstract Underwater Wireless Sensor Networks (UWSNs) are exceedingly critical for large-scale underwater applications, such as environmental monitoring, infrastructure inspection, target tracking, and marine surveillance. Nevertheless, network lifetime and communication reliability are severely constrained by harsh underwater acoustic conditions, limited battery power, large propagation delays, node mobility, and uneven energy consumption. In response to these issues, this study proposes a Climate-Aware Hybrid Clustering and Routing (CA-HCR-UWSN) framework to enable sustainable, long-term underwater monitoring. This work proposes a hybrid framework that combines Elephant Herding Optimization (EHO) with the Gravitational Search Algorithm (GSA) to provide an effective solution to… 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 >

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 >

Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Miao Ye¹, Ziheng Wang¹, Qiuxiang Jiang¹, Xingsi Xue², Wenxi Liu³, Yu Ning¹, Cheng Zhu^1,4,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078282 - 15 June 2026

Abstract In recent years, anomaly detection in Wireless Sensor Networks (WSNs) has been widely studied using Graph Neural Networks and Transformer-based methods. However, in multi-node and multi-modal data scenarios, these approaches still face challenges such as insufficient extraction of spatiotemporal correlation features, limited modeling capabilities when relying solely on either time-domain or frequency-domain information, and high computational overhead. To address these issues, this work aims to develop an anomaly detection model that balances detection performance with computational efficiency, enabling effective identification of complex anomaly patterns. Specifically, we propose a time–frequency feature extraction method with topological information… More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Chedia Jarray², Huda Althumali³, Faten A. Saif ⁴, Raja Azlina Raja Mahmood¹, Mohammed Sani Adam⁵, Nor Fadzilah Abdullah⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077054 - 27 May 2026

Abstract Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection,… More >

Amjad Rehman¹, Tariq Mahmood^1,2, Faten S. Alamri^3,*, Muhammad I. Khan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.076683 - 27 May 2026

Abstract The rapid deployment of Wireless Sensor Networks (WSNs) faces critical challenges due to sensor nodes’ limited energy and communication capabilities, which restrict network lifetime and data transmission efficiency. Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution, whereas intelligent optimization approaches are hindered by high computational costs and slow convergence. This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints, aiming to improve stability, energy efficiency, and throughput. This research proposed three quantum optimization-based solutions to address complex issues. First,… More >