Enhancing Energy Efficiency in Smart Agriculture: GA based Optimization for Large-Scale LoRaWAN Networks

With the increasing popularity of Internet of Things (IoT) based Smart Agriculture, numerous intelligent IoT devices must be interconnected across vast agricultural areas. However, sensor-integrated agricultural IoT devices are unable to establish connections over extensive areas due to the limited radio range of conventional wireless sensor networks, which cannot be expanded. Long-Range Wide-Area Networks (LoRaWAN) is a widely adopted communication protocol that addresses these issues. It enables devices to communicate across large distances while consuming minimal power. Wireless sensor networks (WSNs) combine sensing, connectivity, and computation in an intelligent environment. The limited availability of energy resources provides a major challenge for the LoRaWAN network to tackle. Clustering and routing algorithms present effective ways to mitigate the issue and enhance the network's efficiency. The objective is to enhance energy efficiency and prolong the longevity of sensor nodes inside the LoRaWAN network. This study presents the metaheuristic Genetic Algorithm (GA) for optimizing multiple aspects of wireless sensor networks in precision agriculture, including energy efficiency, the placement of gateways and sensor nodes in extensive WSNs, and the optimization of LoRaWAN network parameters.