Wireless Sensor Networks in the Era of IoT and Next-Generation Networks: A Comprehensive Survey on Energy Efficiency, Security Challenges, and AI-Driven Optimization

Abstract

The WSNs now form one of the backbones of IoT, supporting its application areas in healthcare, environmental consideration, industrial automation and smart cities. However, these WSNs face various challenges, such as energy limitations, data handling issues, security vulnerability issues, and connectivity issues. This survey covers in-depth discussions on advances, challenges, and future directions in WSNs, emphasizing the integration of emerging technologies with Internet of Things (IoT), 5G/6G networks, machine learning (ML), and energy harvesting techniques. ML plays a role in optimizing data aggregation, fault detection, localization, and energy, while AI-based solutions are explored for network performance enhancement. On the other hand, energy harvesting techniques-solar energy, thermoelectric generation, and mechanical energy harvesting-are scrutinized as eco-sustainable ways to enhance the long-lasting capabilities of WSNs. Aspects of WSN integration with 5G/6G are covered, particularly ultra-low latency, dependability for real-time applications. Security-related challenges, including Jamming, Denial-of-sleep, and Routing attacks, have also been studied with proposed countermeasures against them. Future research directions, such as lightweight algorithms, support for mobility, edge computing, and fog computing integration to mitigate computation and energy constraints, have also been identified in the work. In doing so, it integrates the existing research while identifying gaps that serve as a roadmap to the efficient, secure, and scalable development of WSN systems, thus opening up further collaboration across discipline to fully realize them in real-world applications.