The Internet of Things (IoT) is a crucial part of the future Internet. It can obtain and transfer data, making things more
effective. The energy consumption of nodes is a challenge in IoT networks. Innovation in IoT is a dynamic and evolving field. The IoT
plays a significant role in contributing to sustainable cities and economies. Clustering is an IoT data collection strategy that decreases
energy usage by generating clusters out of IoT nodes. The Cluster Head (CH) supervises all Cluster Member (CM) nodes within each
group, enabling the establishment of both intra-cluster and inter-cluster connections. Numerous algorithms are available to extend the
IoT’s remaining energy time, increase the number of nodes that are in an active state, and lengthen the network’s lifespan. These
algorithms use optimization and clustering approaches to improve the network’s overall performance and energy efficiency. In this
paper, a comparison between five algorithms is carried out, which are Low Energy Adaptive Clustering Hierarchy (LEACH), Artificial
Fish Swarm Algorithm (AFSA), Genetic Algorithm (GA), Energy-Efficient Routing using Reinforcement Learning (EER-RL), and
Modified Low Energy Adaptive Clustering Hierarchy (MODLEACH). According to the comparison between the five algorithms, the
AFSA algorithm proved the highest efficacy, yet the GA algorithm remained superior in certain conditions. |
