Reliable Communication Through Energy‐Efficient Congestion Control Mechanism Utilizing Deep Learning and Meta‐Heuristic Optimization in Wireless Multimedia Sensor Networks

ABSTRACT Wireless Multimedia Sensor Networks (WMSNs) are plagued with issues of battery life limitation and congestion, which lead to packet loss, energy consumption, and delay. In this paper, an energy‐aware congestion control architecture named OCNN‐TDO‐WMSN, combining Orthogonal Convolutional Neu...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of communication systems Ročník 38; číslo 16
Hlavní autoři: Felicia, Moses Angeline, Ravindran, Rajamani Samson, Beaulah, Hendry Lilly, Kesavan, Thangaraj
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester Wiley Subscription Services, Inc 10.11.2025
Témata:
Artificial neural networks
Clustering
Congestion
congestion control
Delay
Double Fuzzy Clustering‐Driven Context Neural Network
Energy consumption
Machine learning
Multimedia
Optimization
quality of service
Semi‐Decentralized Energy Routing Algorithm
Tasmanian Devil Optimization
Wireless Multimedia Sensor Network
Wireless networks
Wireless sensor networks
ISSN:1074-5351, 1099-1131
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:ABSTRACT Wireless Multimedia Sensor Networks (WMSNs) are plagued with issues of battery life limitation and congestion, which lead to packet loss, energy consumption, and delay. In this paper, an energy‐aware congestion control architecture named OCNN‐TDO‐WMSN, combining Orthogonal Convolutional Neural Networks (OCNN) and Tasmanian Devil Optimization (TDO), is proposed. The system uses a rate‐based congestion control with cluster routing to reduce energy and delay. Double Fuzzy Clustering‐driven Context Neural Networks (DFCCNN) are employed for clustering, OCNN for cluster head election, and TDO for adaptive packet rate adaptation. Throughput is additionally enhanced through the use of a Semi‐Decentralized Energy Routing Algorithm (SDERA). The proposed method is simulated in NS‐3 and is tested with parameters of energy consumption, lifetime, delay, throughput, packet delivery ratio (PDR), and reliability. Results indicate that OCNN‐TDO‐WMSN reaches a throughput of 50 Kbps and a PDR of 95%, better than the current methods. This work proposes OCNN‐TDO‐WMSN, a congestion control system for Wireless Multimedia Sensor Networks (WMSNs) that integrates Orthogonal Convolutional Neural Networks (OCNN) for cluster head selection and Tasmanian Devil Optimization (TDO) for dynamic packet rate reduction. The framework employs Double Fuzzy Clustering Context Neural Networks (DFCCNNs) for efficient clustering and uses a Semi‐Decentralized Energy Routing Algorithm (SDERA) to enhance throughput. Simulated in NS‐3, the system improves network lifetime, reduces energy consumption and delay, and achieves a throughput of 50 Kbps and a packet delivery ratio of 95%, outperforming conventional approaches.
Bibliografie:The authors received no specific funding for this work.
Funding
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.70254