Energy Conservation Strategies in Wireless Sensor Networks: Surveying Routing, Clustering, and Cross-Layer Optimization Approaches

Abstract

Wireless Sensor Networks (WSNs) are a key part of the modern Internet of Things (IoT), facilitating real time sensing, monitoring, and data transfer in a variety of applications including environmental monitoring, healthcare, industrial automation, smart agriculture and smart cities. The battery capacity constraints of sensor nodes, however, are a major limitation and energy efficiency is an important factor in the design of sensor networks to ensure their operation and reliability over extended periods. A large number of solutions have been proposed to improve the energy consumption and the network lifetime over the years, such as energy-aware routing protocols, clustering, sleep scheduling, data aggregation, and transmission power optimization. Recent developments have also brought in the concept of intelligent solutions such as artificial intelligence, machine learning, game theory, and optimization algorithms to enhance routing decisions and resource utilization. Furthermore, energy harvesting approaches and self-powered sensor networks are becoming attractive approaches for sustainable and long-term deployment. This survey aims to provide an in-depth overview of the recent advances of energy-efficient wireless sensor networks (WSN) during the years 2020-2026. The study includes routing protocols, clustering and deployment strategies, techniques of optimization, energy harvesting techniques, solutions based on artificial intelligence and cross-layer optimization techniques. Additionally, a comparative study of the current methods is given, so as to show their advantages, disadvantages and suitability of their use. The survey highlights the challenges in the current research and talks about the future directions in the development of intelligent, sustainable and energy aware wireless sensor network architectures.