Honey Bee-Inspired Energy-Efficient Cluster Head Optimization for Large-Scale Cloud-based IoT Applications

The fast growth of Internet of Things (IoT) devices has made it necessary to create energy-efficient clustering algorithms in order to improve network performance. This study introduces the Honey Bee-inspired cluster head optimization method (HBCO), which is designed for large-scale cloud-based Internet of Things (IoT) applications. HBCO uses the exploratory and exploitative abilities of scout bees, employed bees, and observer bees to choose cluster heads that are both energy-efficient and dependable. The TOSSIM simulator and the Intel Lab Data dataset (which includes 54 sensors and 2.1 million data points) are used to assess the method. The performance of the proposed model is compared with the existing conventional methods. The conventional clustering methods frequently experience suboptimal cluster head selection, resulting in increased energy expenditure and diminished network lifespan. This paper tackles these difficulties by presenting the Honey Bee-inspired Cluster Head Optimization (HBCO) method, which improves energy efficiency and network stability in extensive cloud-based IoT systems. Experimental findings indicate that HBCO markedly decreases energy consumption by as much as 20%, prolongs network longevity by up to 15%, and enhances packet delivery ratio by up to 10%, establishing it as a formidable option for energy-efficient IoT clustering.