Abstract

Wireless Sensor Networks (WSNs) consist of spatially distributed sensor nodes that communicate wirelessly. In this paper, a novel Generative AI driven Multi-objective Elitist Horse herd Optimization (GAI-MEHO) technique is proposed for resource efficient data transmission in WSN. The main aim of GAI-MEHO technique is to achieve high data transfer with minimal delay. The proposed GAI-MEHO technique includes two processes namely resource efficient sensor node selection and optimal route path discovery. First, GAI-MEHO technique employs a Generative AI model to examine multiple resources of sensor nodes such as residual energy, bandwidth and memory. Based on analysis, resource efficient sensor nodes are classified. After the multiple route paths are established between the sensor nodes. Followed by optimal route path discovery with minimal distance and better link connectivity is identified using Multi-objective elitist horse herd algorithm. Finally, data forwarding is carried out from the source node to the sink or base station through the optimal route path. Experimental evaluation of GAI-MEHO technique is conducted on factors such as energy drain rate, success rate, jitter, data transfer rate and average hop count.

Authors

N. Keerthikaa, R. TamilSelvi
VET Institute of Arts and Science (Co-Education) College, India

Keywords

Wireless Sensor Networks, Resource Efficient Data Transmission, Generative AI Model, Deep Belief Neural Networks, Segmented Regression, Optimal Route Path Discovery