Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN DOI

P. Sakthi Shunmuga Sundaram,

Vijayan Kannabiran

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Summary Internet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between nodes during packet routing. creates unnecessary consumption burdens for those actively transmitting nodes. Conceptually, an effective formation phase supports better routing mechanisms, while sustaining efficiency individual paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme facilitate adaptive energy‐efficient topologies. NFCF utilizes fuzzy logic neural identify optimal super flexible formations. approach enables configurable sizes along with inclusion/exclusion criteria member on thresholds. Parameters evaluated node selection include degree node, expected per cluster, variance, residual energy. Nodes not meeting thresholds are excluded. The network updates rules guide clustering decisions anticipated dynamics under different conditions. performance proposed objective function changes related transmission, variation, variance before after transmissions, averaged end‐to‐end delay across cycles. Results compared against genetic clustering, energy‐aware fuzzy‐based distributed logic‐based multi‐hop weighted k‐means clustering.

Language: Английский

Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks DOI Creative Commons
Guo Li,

H. Y. Sheng

International Journal of Cognitive Computing in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

1
An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks DOI Creative Commons
Yousra Mahmoudi, Nadjet Zioui, Hacène Belbachir

et al.

International Journal of Cognitive Computing in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
An Efficient Cluster Based Routing in Wireless Sensor Networks Using Multiobjective‐Perturbed Learning and Mutation Strategy Based Artificial Rabbits Optimisation DOI Creative Commons

Babiyola Arulanandam,

Khalid Nazim Abdul Sattar, Rocío Pérez de Prado

et al.

IET Communications, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Wireless sensor networks (WSNs) is a wireless system including the set of distributed nodes used for physical or environmental observation. A network energy expenditure considered as significant concern because battery restricted sensors WSN. Clustering and multi hop routing are effective approaches to enhance lifecycle communication. Achieving anticipated objective reducing expenditure, thereby increasing lifecycle, an optimisation issue. In recent times, nature inspired meta‐heuristic extensively utilised solving different issues. this context, research aims accomplish by proposing multiobjective‐perturbed learning mutation strategy based artificial rabbits namely M‐PMARO optimum cluster head (CH) selection route discovery. The proposed incorporates experience perturbed (EPL) identify capable regions over search space enhancing exploration avoiding local optima To formulate multiobjective, residual energy, average intracluster distance, base station (BS) CH balancing factor (CHBF) node centrality incorporated discovery while BS distance routing. analysed on alive nodes, dead throughput data received in lifecycle. viability validated comparing it with existing such fitness glowworm swarm fruitfly algorithm (FGF), balanced particle (EBPSO), improved bat (IBOA), graph neural (GNN) fuzzy logic (PSO) clustering protocol PFCRE. count 100 1200 rounds, which higher than EBPSO.

Language: Английский

Citations

0
An Efficient and High-Performance WSNs Restoration Algorithm for Fault Nodes Based on FT in Data Aggregation Scheduling DOI Creative Commons
Cheng Li, Guoyin Zhang

International Journal of Cognitive Computing in Engineering, Journal Year: 2025, Volume and Issue: 6, P. 508 - 515

Published: May 4, 2025

Language: Английский

Citations

0
Optimal Routing in Wireless Sensor Networks for Advancing IoT Efficiency and Sustainability using Enhanced Ant Colony Algorithm with machine learning approaches DOI Creative Commons
A. Harshavardhan

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(2s), P. 922 - 930

Published: April 4, 2024

This research study aims to investigate the incorporation of machine learning tools, such as Q-learning, Genetic Algorithms, Unsupervised Learning, and Ensemble into Enhanced Ant Colony Algorithm assess impacts on WSN’s performance. Ten experimental trials were conducted each analyze accuracy, precision, F1 score results. It was observed that Q-learning achieves an average accuracy 0.867; precision 0.842; 0.854, making it highly adaptable efficient in routing decisions. The GA presented 0.833; 0.812; 0.821 which show tool is robust evolutionary optimization. performances indicate mean for model are 0.875, 0.856, 0.865, respectively . As ES with multi source models, showed highest performance 0.898, 0.882, 0.891 score, thus very valuable backend regarding application tools optimization algorithms efforts geared towards WSN efficiencies sustainability..

Language: Английский

Citations

1
Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN DOI

P. Sakthi Shunmuga Sundaram,

Vijayan Kannabiran

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Summary Internet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between nodes during packet routing. creates unnecessary consumption burdens for those actively transmitting nodes. Conceptually, an effective formation phase supports better routing mechanisms, while sustaining efficiency individual paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme facilitate adaptive energy‐efficient topologies. NFCF utilizes fuzzy logic neural identify optimal super flexible formations. approach enables configurable sizes along with inclusion/exclusion criteria member on thresholds. Parameters evaluated node selection include degree node, expected per cluster, variance, residual energy. Nodes not meeting thresholds are excluded. The network updates rules guide clustering decisions anticipated dynamics under different conditions. performance proposed objective function changes related transmission, variation, variance before after transmissions, averaged end‐to‐end delay across cycles. Results compared against genetic clustering, energy‐aware fuzzy‐based distributed logic‐based multi‐hop weighted k‐means clustering.

Language: Английский

Citations

0