Due to the dynamic nature of the network architecture, resource constraints, and susceptibility to security attacks, securing data transmission in Mobile Ad-hoc Networks (MANETs) is a significant problem. This work proposes a novel Equivariant Quantum Neural Networks with Adaptive Tangent Brakerski-Gentry Vaikuntanathan Homomorphic Encryption algorithm (EQNN-ATBGVHEA)- based secure routing in MANET. The suggested approach comprises three steps: cluster head (CH) selection, optimal path selection, and secure data transfer. Initially, the Bowerbird Optimization Algorithm chooses the CH and sends the message through the constructed path. Once the clusters are established, data is transferred between the sender and receiver. For optimal route selection, developed the EQNNs technique which incorporates a neural network for quick route selection. EQNN resolves the issues of local optimality by constructing a new fitness process based on residual energy (RE) and delay. After the optimal path selection, Data transfer is secured by the innovative ATBGVHEA technique. Furthermore, this method is built using NS3, and the variables are determined. Additionally, the acquired results are contrasted with existing approaches for validating the efficiency of the suggested strategy. The developed method achieved a clustering accuracy of 98.5%, a computational time of 55ms, and a residual energy of 0.44.

Wireless Mesh Networks (WMNs) have gained prominence in modern communication technology due to their flexibility and ease of deployment, which are advantageous in scenarios like disaster management and rescue operations. However, existing methods for enhancing the performance of WMNs, such as increasing the number of gateways, are costly, introduce interference, and complicate deployment. Moreover, current routing protocols often suffer from suboptimal packet delivery due to inadequate traffic flow management and packet loss. This research addresses these gaps by proposing a novel optimization model that integrates Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) techniques to enhance packet delivery ratio in WMNs using Voice over Internet Protocol (VoIP). Unlike traditional approaches that overlook efficient traffic management, our proposed model focuses on optimizing packet transmission by selecting efficient routes and minimizing packet loss. The novelty of this solution lies in its hybrid use of ABC and PSO for dynamic node and route selection, which significantly improves network performance, reduces control overhead, and minimizes packet loss. Experimental results demonstrate that the proposed model outperforms existing protocols, making it a promising approach for enhancing network reliability and efficiency in WMNs.

Data packets in Wireless Mesh Networks (WMNs) are routed across several nodes in a multi-hop fashion. The Quality of Service (QoS), seamless connectivity, reliability, and scalability of Wireless Mesh Networks are all significantly impacted by routing approaches. Routing protocols should enforce the fair utilization of resources i.e. bandwidth or channel among network nodes irrespective of their spatial location from the Gateway. The two-hop or multi-hop nodes in wireless mesh networks experience resource starvation due to the functioning of the MAC protocol and TCP/TP networking protocol. The Starvation issue has a significant impact on the QoS requirements of wireless mesh networks. It is known that using appropriate scheduling techniques in network planning substantially minimizes starvation. To reduce the starving of resources to the multi-hop network nodes, novel optimized routing algorithms have been proposed and implemented in this work. To address the starvation, a GA-based cross-layer optimized scheduling method that operates at the MAC and Network layers is implemented. A hybrid approach that combines the features of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) is also implemented to solve the local minimum problem in GA. Results show that the suggested optimization methods greatly improve the fairness performance of wireless mesh networks.