Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory Strategy-Directed MADDPG Algorithm

Abstract

This paper presents an in-depth study of the application of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms with an exploratory strategy for duty cycle scheduling (DCS) in the wireless sensor networks (WSNs). The focus is on optimizing the performance of sensor nodes in terms of energy efficiency and event detection rates under varying environmental conditions. Through a series of simulations, we investigate the impact of key parameters such as the sensor specificity constant α and the Poisson rate of events on the learning and operational efficacy of sensor nodes. Our results demonstrate that the MADDPG algorithm with an exploratory strategy outperforms traditional reinforcement learning algorithms, particularly in environments characterized by high event rates and the need for precise energy management. The exploratory strategy enables a more effective balance between exploration and exploitation, leading to improved policy learning and adaptation in dynamic and uncertain environments. Furthermore, we explore the sensitivity of different algorithms to the tuning of the sensor specificity constant α, revealing that lower values generally yield better performance by reducing energy consumption without significantly compromising event detection. The study also examines the algorithms' robustness against the variability introduced by different event Poisson rates, emphasizing the importance of algorithm selection and parameter tuning in practical WSN applications. The insights gained from this research provide valuable guidelines for the deployment of sensor networks in real-world scenarios, where the trade-off between energy consumption and event detection is critical. Our findings suggest that the integration of exploratory strategies in MADDPG algorithms can significantly enhance the performance and reliability of sensor nodes in WSNs.