A Hybrid Algorithm for Optimising Power Consumption of Wireless Sensor Networks in Precision Agriculture

ABSTRACT Recently, precision agriculture has used wireless sensor networks (WSNs) to gain valuable insights and improve crop yields, promoting efficient resource use and data‐driven decisions. However, WSNs face challenges, such as high power consumption from continuous sensing, data processing and communication, especially in large‐scale setups, which limits their lifespan. This paper focuses on reducing power use in agricultural WSN sensor nodes during data transmission of soil moisture, rainfall, light intensity, air temperature and humidity from the transmitting sensor node to the base station. Four algorithms are proposed to cut power consumption. First, a sleep/wake (S/W) scheme using a simple duty cycle called S/W‐DC. Second, the S/W scheme combined with adaptive data sampling (ADS) based on redundant data (RD), called S/W‐ADS‐RD. Third, the S/W scheme integrated with dynamic voltage scaling (DVS), named S/W‐DVS. Fourth, a hybrid of all three, called S/W‐ADS‐RD‐DVS. The sensor uses a 12 V/5 W solar panel for energy harvesting to maintain operation. The hybrid algorithm achieved 99.232% power savings and extended battery life to approximately 1.83 years. During a 6‐h session, data transmission was reduced by 99.93%. This research could significantly improve WSN efficiency in precision agriculture and can be applied to energy‐efficient WSN deployment across various fields, supporting Internet of Things (IoT) applications.