Hybrid Sleep Scheduling for Energy-Efficient IoT Sensor Networks in Smart Poultry Monitoring

Abstract

The integration of Internet of Things (IoT) technologies into precision poultry farming has revolutionized environmental monitoring; yet, high energy consumption in sensor networks remains a significant barrier to scalability and sustainability. This study presents a hybrid sleep scheduling algorithm for energy-efficient IoT-based poultry environmental monitoring. The algorithm enables dynamic transitions between Active, Modem Sleep, and Light Sleep modes according to environmental stability and data variability. Analytical models of system cycle time and power consumption were developed to optimise node behaviour under varying farm conditions. A prototype built with Wemos D1 Mini microcontrollers, DHT22, and MQ135 sensors was experimentally validated in a live poultry environment. Results show an average energy reduction of 68.4% compared to always-active systems, while maintaining latency below 2 seconds and measurement errors within ±0.4°C, ±1.3% RH, and ±7 ppm. The proposed framework offers a scalable, low-power architecture suitable for remote, battery-powered farms, advancing the sustainability of IoT-enabled livestock management and supporting the United Nations’ SDGs 2 and 12.