Optimized Wireless Sensor Network Clustering Using a Hybrid Firefly–Bat Algorithm (HFBA)

Abstract

Wireless Sensor Networks (WSNs) are composed of numerous sensor nodes that collaborate to observe and evaluate various environmental parameters, transmitting the data to a central base station. Each node is equipped with a unique transmitter, receiver, and processor to facilitate sensing, processing, and communication. These networks are often installed in extremely remote and inaccessible areas. A significant challenge arises from the processor's energy consumption constraints, as the sensor nodes possess very limited battery life. Selecting cluster heads has proven to be an effective strategy for enhancing energy efficiency, as it decreases network traffic and, through data aggregation, extends the networks' operational lifespan while minimizing energy use. This paper introduces the Hybrid Firefly-Bat Algorithm (HFBA) as an innovative method for energy-efficient cluster head selection. HFBA combines the rapid convergence of the Firefly Algorithm with the Bat Algorithm's global search capability. Simulation results indicate that HFBA outperforms traditional methods, such as LEACH (Low-Energy Adaptive Clustering Hierarchy). Specifically, HFBA reduces total energy consumption by 23.6% compared to LEACH and increases the networks' operational lifespan by an average of 1.45 times. Additionally, there is a 14.3% enhancement in the data packet delivery ratio, and node stability improves by 12.8%. These results underscore HFBA's effectiveness in optimizing the energy consumption balance and maintaining a longer operational duration in energy-constrained WSN environments. Furthermore, the algorithm works without location data, streamlining the cluster formation process while remaining adaptable to different deployment situations