EGA-DAS: ENHANCED GENETIC ALGORITHM WITH EFFECTIVE DATA AGGREGATION SCHEME FOR AVOIDANCE OF DATA FLOODING IN WSN

Abstract

In Wireless Sensor Networks (WSNs), efficient routing is essential for maintaining network reliability and optimizing energy consumption. This paper introduces Enhanced Genetic Algorithm with Data Aggregation scheme (EGA-DAS) method a novel route discovery approach combining Electro-Magnetism with Enhanced Genetic Algorithm (EMEGA) to enhance routing efficiency. EMEGAutilizes electromagnetic principles for node attraction and disgust, simulating the optimization process similar to genetic algorithms. By iteratively refining routes based on energy consumption and network conditions, EMEGA aims to discover paths that minimize transmission costs and ensure robust communication. Performance evaluations demonstrate EMEGA's capability to achieve superior routing efficiency compared to traditional methods, offering promising advancements for reliable and energy-efficient WSN deployments. Extensive simulations and performance evaluations demonstrate that the proposed data aggregation scheme significantly outperforms existing methods in terms of energy efficiency, delivery delay, and data accuracy. This study contributes to the advancement of WSN technologies by offering a robust solution to the challenges of data flooding and delivery delay, making the way for more efficient and sustainable network operations.