Abstract
Wireless Sensor Networks (WSNs) have revolutionized applications like environmental monitoring, healthcare, disaster management, and homeland security. Despite advancements, challenges persist in energy efficiency, network overhead, and scalability for real-world scenarios. Recent innovations, such as the FALM system, have achieved notable improvements, including a 25.04% reduction in energy consumption, a 21.72% decrease in network overhead, and a 14.81% increase in node lifespan compared to the BSPK model, highlighting the potential of advanced routing algorithms. Future research must focus on real-world testbeds to validate the robustness and scalability of WSN algorithms under diverse conditions like high node density and dynamic traffic. For energy-intensive applications such as multimedia data transfer, maintaining energy efficiency without compromising Quality of Service (QoS) is crucial. Natureinspired algorithms like Particle Swarm Optimization (PSO) and Sparrow Search Algorithm (SSA) offer promising solutions by optimizing routing paths and resource allocation. Integrating WSNs with emerging technologies could further enhance their capabilities. The Internet of Things (IoT) fosters connectivity, machine learning models enable predictive adaptations, and blockchain secures communications against unauthorized access. Additionally, expanding performance evaluation metrics to include end-to-end delay, packet delivery ratio, and scalability will ensure comprehensive optimization. These strategies pave the way for developing robust, energy-efficient, and adaptive WSN architectures that meet the demands of modern applications, ensuring long-term viability and enhanced performance.
Published Version
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