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 >

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 >

Xiaoping Yang^1,*, Songjie Yang², Junqi Long¹, Quanzeng Wang³, Bin Yang⁴, Xiaofang Cao⁵, Guochao Qi⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078105 - 08 May 2026

Abstract The rapid proliferation of latency-sensitive applications, coupled with the limitations of service range, has driven the integration of aerial simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASTAR-RIS) and task offloading to enhance both communication and computational efficiency in wireless sensor networks (WSNs). However, in WSNs, conventional ASTAR-RIS-assisted task offloading faces critical limitations, including restricted endurance, underutilized network caching and computing resources, and inefficient resource allocation within the optimization framework. To overcome these challenges, this paper integrates wireless power transfer (WPT) technology and proposes a novel energy-efficient ASTAR-RIS and WPT-assisted task offloading and content caching framework… More >

Reshma Siyal¹, Jun Long^1,*, Muhammad Asim^2,*, Mudasir Ahmad Wani³, Kashish Ara Shakil⁴, Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074282 - 08 May 2026

Abstract The rapid growth of the Internet of Things (IoT) has led to dense wireless sensor networks (WSNs) deployed in critical applications such as smart cities, industrial monitoring, and healthcare. However, energy constraints, unpredictable communication delays, and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan. Traditional approaches often fail to balance delay minimization with energy efficiency, especially in large-scale or dynamic networks. To address these issues, this study proposes CTSO-DRNN, a novel framework that integrates Chronological Tangent Search Optimization (CTSO) with a Deep Recurrent Neural Network (DRNN) for accurate delay prediction… More >

Ho Hai Quan^1,2, Le Huu Binh^1,*, Nguyen Dinh Hoa Cuong³, Le Duc Huy⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075549 - 12 March 2026

Abstract Wireless Sensor Networks (WSNs) play a crucial role in numerous Internet of Things (IoT) applications and next-generation communication systems, yet they continue to face challenges in balancing energy efficiency and reliable connectivity. This study proposes SAC-HTC (Soft Actor-Critic-based High-performance Topology Control), a deep reinforcement learning (DRL) method based on the Actor-Critic framework, implemented within a Software Defined Wireless Sensor Network (SDWSN) architecture. In this approach, sensor nodes periodically transmit state information, including coordinates, node degree, transmission power, and neighbor lists, to a centralized controller. The controller acts as the reinforcement learning (RL) agent, with the… More >