A Reliable and efficient data transfer on wireless sensor networks by using Optimization algorithms

Abstract

Wireless Sensor Network technologies that require collecting all node-generated data without any losses like structural monitoring. Since wireless networks and resource constraints present additional challenges, point-to-point transmission, which is employed in the network for sustainable communication, becomes extremely ineffective in Wireless Sensor Networks. We investigate elements that determine reliability and explore effective combinations of possible solutions. You can implement information redundancy techniques like rebroadcasting and destruction of data techniques. Additionally, route maintenance, which explores an alternate hop and several faults, minimizes packet drops. In this paper, we evaluated several machine learning optimization algorithms for efficient and reliable data transfer in wireless networks. Our simulation results prove that each opportunity overcomes different types of flaws that occurred by the existing techniques. The Arithmetic Optimization Algorithm, Particle Swarm Optimization, and Shuffled Complex Evolution Algorithm is efficient in obtaining reliability, achieving very high reliability by enduring packet losses, and it responds very instantly to link failures. That’s the proposed combination of optimization algorithms that can prove more than 90% memory consumption with Time, High Speed, and Accuracy by providing an alternative to point-to-point communication over multiple nodes in the network.