Nature-Inspired Algorithms to perform Localization and Security in Wireless Sensor Networks

This research article embarks on a dual-pronged journey, ardently addressing the challenges of both localization enhancement and security fortification in the context of wireless sensor networks (WSNs). The study introduces a holistic approach, employing nature-inspired computing algorithms - namely Particle Swarm Optimization (PSO) and Firefly Optimization (FF) - to diligently tackle the intricate issue of localization errors. To amplify the potency of these algorithms, hybrid variations are ingeniously crafted, ushering in heightened effectiveness. Furthermore, the research article undertakes a visionary stride by unveiling a hybridized system that harmoniously merges two stalwart cryptographic forces - Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES). This fortified system stands poised to bolster data security within WSNs, aligning seamlessly with the overarching goals of the research. Through meticulous simulated analyses, the article meticulously unveils the tangible outcomes of these dual approaches. The research validates the efficacy of these innovative methods in real-world scenarios, painting a vivid picture of their collective impact. The findings resoundingly underscore the potential of these approaches to revolutionize the realms of accuracy, efficiency, and security across an array of WSN applications. As the modern landscape continues to demand both enhanced precision and robust security measures, this research article emerges as a beacon of innovation. By synergizing localization enhancements and security fortifications through nature-inspired algorithms and hybrid cryptographic methodologies, the study unfolds a comprehensive strategy that holds profound implications for the trajectory of WSNs.