In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed workload among various nodes equally in a situation where some nodes are under load and some are overload. Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005. It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This survey paper is a comprehensive study about load balancing in cloud computing using the ABC algorithm. It also defines some basic concept about swarm intelligent and its property.