Improved Butterfly and Fuzzy Logic with Falcon Optimization Algorithm based Routing Protocol in WSN

Abstract

Introduction: Wireless sensor networks (WSNs) are widely used in real-time applications, but optimizing node energy use and network lifetime are challenging. This issue is solved through clustering and cluster head (CH) selection, and efficient routing protocols enhance performance and decrease energy use.

Objectives: To propose a hybrid optimization approach for minimizing energy consumption and increasing network lifeline. For efficient data transmission, it optimizes the selection of Cluster Head (CH).

Methods: An optimization-based clustering and path selection method for data transmission in WSNs. Cluster heads (CHs) are selected using based on energy, distance, node degree, and centrality. Fuzzy Logic and F-FOA optimize data routing by evaluating node costs and determining the best path using pheromone-based probabilistic selection.  

Results: The proposed model exhibits better throughput, lower latency, packet delivery ratio, significantly lower energy consumption and packet loss compared to existing methods (ASFO, GJO and ESO). The results show that it works well to improve WSN.

Conclusions: This study enhances WSN lifetime by reducing energy consumption. CH selection using IBO and routing via FFOA improve efficiency, while IBFFOA minimizes energy use and processing time. Simulations show a 28% reduction in processing time, 26% lower energy consumption, and an 8% increase in clustering accuracy compared to existing models.