Enhancing Efficiency and Resilience in Wireless Sensor Networks Through Advanced Deep Reinforcement Learning Strategies

Abstract

This paper takes the first step in establishing a new avenue of research to include Deep Reinforcement Learning (DRL)-based strategies toward enhanced operational efficiency, resilience, and adaptation of WSNs. With the dual pressure of stringent energy constraints and unpredictable deployment environments, WSNs require new methods to optimize their performance and reliability. This study applied the full power of DRL by custom-developing intelligent algorithms for real-time decision-making to further complement energy resource management, data route protocol optimization, and highly reliable network performance under dynamic conditions. The study provides evidence of well-designed methodological frameworks including simulation environments emulating complexities in actual WSNs to demonstrate DRL's capacity to save energy by 30% and improve network resilience to changing environmental or intentional disruptions while achieving scalable and adaptive network management. The results validate the role of DRL in transforming WSN optimization and yield insights and methodologies beneficial for similar challenges across varied fields. This paper presents an in-depth account of what has happened recently in the field, takes a systematic view of the implementation of DRL strategies through empirical investigations, and highlights future directions for research. It indicates an excellent step towards broadening the participatory reach of all interconnected technologies, and thus a very serious advance in the intelligent autonomous sensor networks will be recorded in history.