Reliable Communication Through Energy‐Efficient Congestion Control Mechanism Utilizing Deep Learning and Meta‐Heuristic Optimization in Wireless Multimedia Sensor Networks - Felicia, Moses Angeline; Ravindran, Rajamani Samson; Beaulah, Hendry Lilly; Kesavan, Thangaraj (International journal of communication systems, 2025)
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.