A Comprehensive Investigation of Energy-Efficient Wireless Sensor Networks in Consideration of Optimization and Scheduling Techniques

This chapter delves into the critical aspects of energy-efficient wireless sensor networks (WSNs), emphasizing optimization and scheduling techniques to maximize performance and extend network longevity. It begins by addressing the challenges of power consumption in remote sensor deployments and explores various factors influencing WSN limitations, including network security threats and data collection efficacy. The chapter also examines deployment techniques, coverage problems, and network architecture, providing a comprehensive overview of the current landscape of WSNs. A significant portion of the text is dedicated to research background, highlighting recent studies and advancements in AI and machine learning applications for WSN optimization. The chapter presents a detailed simulation using MATLAB to evaluate the energy gain of optimal scheduling methods, comparing it with traditional TDMA protocols. The results demonstrate substantial energy savings, particularly at higher data rates. Additionally, the chapter discusses the integration of AI-driven encryption architectures, energy-aware intrusion detection, and secure routing mechanisms. It concludes by suggesting future directions, such as real-time hardware implementations and the application of machine learning algorithms to further enhance WSN performance.