Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Abdulaziz Yagoub Barnawi²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3213-3250, 2025, DOI:10.32604/cmes.2025.066589 - 30 September 2025

Abstract Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks (WSNs), especially in security-critical, time-sensitive applications. However, most existing protocols degrade substantially under malicious interference. We introduce iSTSP, an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust, precise synchronization even in hostile environments: (1) trust preprocessing that filters node participation using behavioral trust scoring; (2) anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time; (3) reliability-weighted consensus that prioritizes high-trust nodes during time aggregation; and (4) convergence-optimized synchronization… More >

David S. Bhatti^1,*, Annas W. Malik², Haeung Choi¹, Ki-Il Kim^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2157-2177, 2025, DOI:10.32604/cmc.2025.068360 - 29 August 2025

Abstract Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies, limiting their use for lightweight, secure image encryption in resource-constrained Wireless Sensor Networks (WSNs). We propose the SPCM, a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions, leveraging a piecewise function to achieve a broad chaotic range () and a high Lyapunov exponent (5.04). Validated through nine benchmarks, including standard randomness tests, Diehard tests, and Shannon entropy (3.883), SPCM demonstrates superior randomness and high sensitivity to initial conditions. Applied to image encryption, SPCM achieves 0.152582 s (39% faster than some techniques) and 433.42 More >

Deyu Lin^1,2, Yujie Zhang ², Zhiwei Hua², Jianfeng Xu^2,3,*, Yufei Zhao¹, Yong Liang Guan¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1859-1882, 2025, DOI:10.32604/cmc.2025.066283 - 29 August 2025

Abstract Energy efficiency is critical in Wireless Sensor Networks (WSNs) due to the limited power supply. While clustering algorithms are commonly used to extend network lifetime, most of them focus on single-layer optimization. To this end, an Energy-efficient Cross-layer Clustering approach based on the Gini (ECCG) index theory was proposed in this paper. Specifically, a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election (GICHE) is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters. In addition, to improve inter-cluster energy efficiency, a Queue synchronous More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using 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 >

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Amir Mosavi^3,4,5

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 >

Aadil Mushtaq Pandith¹, Manni Kumar², Naveen Kumar³, Nitin Goyal^4,*, Sachin Ahuja², Yonis Gulzar⁵, Rashi Rastogi⁶, Rupesh Gupta⁷

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >

Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >

Chao-Hsien Hsieh¹, Qingqing Yang^2,*, Dehong Kong², Fengya Xu², Hongmei Wang²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2869-2891, 2025, DOI:10.32604/cmc.2024.058122 - 17 February 2025

Abstract The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of… More >

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 >