Energy Enhancement in Multipath Routing Protocol Based Antnet and Artificial Intelligent Model in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are characterized by severe energy constraints, dynamic topology, and limited computational resources, making routing design a critical challenge. Traditional single-path and static routing protocols often lead to uneven energy consumption and premature node failures, thereby reducing network lifetime. To address these limitations, this paper proposes an energy-aware multipath routing protocol that integrates AntNet with a lightweight Multilayer Perceptron (MLP) model. Unlike existing artificial neural network-ant colony optimization (ANN-ACO) or deep learning based routing approaches, the proposed method does not embed complex learning mechanisms into the routing core. Instead, the MLP model is used as an auxiliary decision-support component to assist AntNet in selecting energy-efficient and reliable paths while preserving low computational overhead. The routing decision process considers residual energy, end-to-end delay, packet delivery ratio, and routing overhead, enabling a balanced trade-off between energy efficiency and communication performance. The proposed protocol is evaluated using the NS2.35 simulator under different network densities and traffic conditions. Simulation results demonstrate that the proposed approach reduces energy consumption by up to 32% and routing overhead by 28%, while improving packet delivery ratio by 40% and network lifetime by 22% compared to conventional Ad hoc On-Demand Distance Vector (AODV) and AntNet-based routing protocols. These results confirm that combining AntNet with a lightweight MLP yields an effective and scalable solution for energy-efficient multipath routing in WSNs, without the complexity of deep learning-based schemes.