Recent research in energy-efficient sleep and wake-up scheduling in Wireless Sensor Networks (WSNs) focuses on optimizing node activity cycles to extend network lifetime without compromising communication reliability. Advanced techniques such as adaptive duty-cycling, reinforcement learning, and game theory-based coordination enable nodes to intelligently determine their sleep and wake intervals based on network conditions, energy levels, and traffic demands. Many studies integrate these approaches with coverage preservation and data transmission efficiency, ensuring minimal energy consumption while maintaining sensing accuracy and connectivity. Overall, current research trends emphasize decentralized, context-aware, and self-optimizing scheduling algorithms to achieve sustainable energy management in large-scale WSN deployments.