Smart Energy Guardians: Neural Network Empowered Routing in Wireless Sensor Networks for IoT

Wireless Sensor Networks (WSNs) play a pivotal role in the era of Internet of Things (IoT), enabling seamless data collection and transmission. However, the limited energy resources of sensor nodes pose significant challenges to the longevity and sustainability of WSNs. This research paper presents a comprehensive study on increasing the network lifespan by employing effective strategies in Cluster Head (CH) selection and routing phases.The primary objective is to enhance the performance and efficiency of IoT-WSNs by optimizing crucial aspects such as data aggregation, energy utilization, and decision-making processes. To achieve this objective, proposed model employs an energy-dependent CH selection algorithm for choosing the most suitable CH in the network based on their energy ratings. Moreover, a Neural Network (NN) based Machine Learning (ML) model have been used for effectively selecting the route for data transmission to sink node. The optimal selection of route reduces node’s power consumption as well as enhances the lifespan of the network. The functioning of proposed NN model was evaluated and validated by comparing with traditional approaches in MATLAB Software. Experiment results showcased that proposed NN model is outperforming traditional LEACH, EDEEC and ELEACH models in terms of time consumption, dead nodes and other lifetime evaluation parameters.