Optimized Clustering and Routing with Sink Mobility for Enhanced Lifetime in IoT-based Wireless Sensor Networks for Smart Farming

The rise of the Internet of Things (IoT) is due to the wide usage of mobile devices, cloud computing, data analytics, and other technological advancements. This has led to the integration of sensors, microcontrollers, and transceivers into everyday objects, particularly in IoT-enabled smart farming. Wireless Sensor Network (WSN) nodes are essential as it enables real-time data collection on parameters like temperature and soil moisture. This allows farmers to remotely monitor their fields and address issues such as damage caused by animals. However, the effectiveness of WSN can be hindered by factors like low bandwidth and sub-optimal energy optimization. To address these challenges, the proposed study introduces the Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) clustering algorithm. The algorithm aims to optimize energy usage and enhance decision-making by evaluating solutions based on factors like Throughput, Number of Alive Nodes, Residual Energy, and Cluster count. The study also integrates Sink Mobility in MOCLPSO, enabling the central sink node to move towards remote sensor nodes, thereby improving communication efficiency and reducing energy consumption for data transmission.