Multi-objective cluster head based energy efficient routing using THDCNN with hybrid capuchin search and woodpecker mating algorithm in WSN

Wireless Networks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 18, 2025

Язык: Английский

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Blockchain 6G-Based Wireless Network Security Management with Optimization Using Machine Learning Techniques

Sensors, Год журнала: 2024, Номер 24(18), С. 6143 - 6143

Опубликована: Сен. 23, 2024

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks satisfy. The limitations of have been apparent with the deployment more and networks, which certainly encourages investigation as answer future. This research includes fundamental privacy security issues related technology. Keeping an eye on real-time systems requires secure wireless sensor (WSNs). Denial service (DoS) attacks mark a significant vulnerability WSNs face, they can compromise system whole. proposes novel method in blockchain 6G-based management optimization using machine learning model. In this research, deployed is carried out user datagram transport protocol reinforcement projection regression. Then, completed artificial democratic cuckoo glowworm remora optimization. simulation results based various parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, accuracy. order minimise traffic, it also offers capacity determine optimal node path selection data transmission. proposed technique obtained 97% 95% 96% accuracy, 50% 94% ratio.

Язык: Английский

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Self-adaptive and content-based scheduling for reducing idle listening and overhearing in securing quantum IoT sensors

Internet of Things, Год журнала: 2024, Номер 27, С. 101312 - 101312

Опубликована: Авг. 1, 2024

Язык: Английский

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Optimizing Energy-Efficient Cluster Head Selection in Wireless Sensor Networks using a Binarized Spiking Neural Network and Honey Badger Algorithm

Knowledge-Based Systems, Год журнала: 2024, Номер 299, С. 112039 - 112039

Опубликована: Июнь 12, 2024

Язык: Английский

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Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks

Mathematical Biosciences & Engineering, Год журнала: 2024, Номер 21(3), С. 4587 - 4625

Опубликована: Янв. 1, 2024

<abstract><p>Cluster routing is a critical approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result depletion network energy. For this purpose, we introduce new strategy for clustered that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In selection process heads, excitation function to evaluate select more suitable candidate heads by establishing correlation between energy node positional relationship nodes. addition, (BWO) algorithm has been incorporating cosine factor t-distribution enhance its local global search capabilities, as well improve convergence speed ability. path, use Prim's construct spanning tree DPR determining optimal route based on distances heads. This effectively shortens path enhances stability. Simulation results show survival cycle reduce average consumption network.</p></abstract>

Язык: Английский

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Editorial: The New Era of Computer Network by using Machine Learning

Mobile Networks and Applications, Год журнала: 2023, Номер 28(2), С. 764 - 766

Опубликована: Март 7, 2023

Язык: Английский

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Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused clustering Protocol for Wireless Sensor Networks

Applied Soft Computing, Год журнала: 2024, Номер 157, С. 111456 - 111456

Опубликована: Март 5, 2024

Язык: Английский

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Pizzza: A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks

IEEE Access, Год журнала: 2023, Номер 11, С. 68200 - 68215

Опубликована: Янв. 1, 2023

The widespread employment of wireless sensor networks in various fields necessitates the urgent creation methods for prevailing over known shortcomings this network category. Energy shortage as one most restrictive deficiencies employed sensors category has encouraged many researchers from both academic and industry communities to propose efficient solutions contribute efforts done with aim decreasing energy consumption consequently increasing networks' lifetime. Among bunches schemes proposed regard, cluster-based routing protocols have demonstrated promising results so far. Plenty these improved communication minimized delay, however, they still need be crucial aspects which were proposed, namely reduction lifetime prolongation. Considering all pivotal points, a novel hierarchical protocol, named Pizzza, is introduced paper. Pizzza creatively designed by forming minimum spanning trees among communicating nodes each sector-shape cluster, where only eligible first level architecture can undertake cluster head leading role. Employment innovative scheme concluded prolongation through wastage resulting elimination reverse data flow BS, transmission nearest neighbors, balanced network. resulted 65.52% 77.05% enhancement residual compared selected set popular protocols.

Язык: Английский

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HOSNA: Boosting Smart Agriculture Efficiency With The Hybrid Optimization-Based Sensor Node Activation Model

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 509 - 522

Опубликована: Янв. 3, 2025

Smart agriculture leverages Wireless Sensor Networks (WSNs) to monitor environmental parameters such as soil moisture, temperature, and humidity, enabling precision farming efficient resource utilization. The Hybrid Optimization-Based Node Activation (HOSNA) model designed enhance the efficiency lifespan of (WSN) in smart applications. HOSNA integrates clustering, energy-efficient activation, hybrid optimization algorithms, machine learning optimize sensor node operations while ensuring accurate real-time monitoring. employs Genetic Algorithm (GA) Particle Swarm Optimization (PSO) determine optimal activation schedules, reducing energy consumption prolonging network lifetime. Additionally, a Long Short-Term Memory (LSTM) neural predicts changes, allowing proactive activation. Simulation results demonstrate that achieves 94.0% data accuracy after 1000 operational rounds, surpassing LEACH (90.0%), PEGASIS (86.0%), Random Duty Cycling (RDC) (70.0%). Energy reduced by 24% compared LEACH, lifetime extended 32% over PEGASIS. These highlight HOSNA’s ability provide reliable, energy-efficient, scalable solutions for agriculture. Future improvements could involve adapting heterogeneous networks integrating solar-powered nodes sustainable energy.

Язык: Английский

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Energy‐Efficient Communication Using Auto‐Associative Polynomial Convolutional Neural Network in WSN

International Journal of Communication Systems, Год журнала: 2025, Номер 38(4)

Опубликована: Янв. 19, 2025

ABSTRACT Several changes have been implemented over the years to provide better resource management and service delivery for artificial wireless sensor networks (WSNs) that rely on Internet of Things (IoT). Here, 5G offer high data rates with ultra‐low latency robust reliability, which is essential managing substantial volumes generated by IoT devices in WSNs. needs an optimal communication network transmit among different devices. The whole categorized as heterogeneous clusters clustering. cluster head (CH) selection achieves proficient sink node through chosen CH. In this manuscript, energy‐efficient using auto‐associative polynomial convolutional neural WSN (EEC‐HAPCNN) proposed improved transmission selected route. Initially, clustering done parallel adaptive canopy k‐means (PaC‐k‐M) algorithm. Then, Tasmanian devil optimization algorithm (TDOA) selects CH required facilitating capacity low features 5G. are given utilizing hierarchical (HAPCNN) efficient routing network. EEC‐HAPCNN method NS‐3 (network simulator 3). approach examined performance metrics like throughput, energy consumption, lifetime, number nodes alive. provides 17.45%, 17.63%, 18.43% lesser consumption 17.64%, 18.54%, 19.33% greater life time compared existing DBN‐MRFO‐5G‐WSN, IDCNN‐t‐DSBO, DACP‐WSN‐ANN, EEO‐IWSN‐ML, EECA‐ML‐WSN techniques.

Язык: Английский

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