Optimizing Energy Efficiency in Wireless Sensor Networks Using Genetic Algorithm-Based Routing Protocols

Wireless Sensor Networks (WSNs) have become a revolutionary breakthrough in achieving a vast range of applications from environmental monitoring systems to smart cities. As with any wireless technology, they are limited by a number of energy resources which creates certain challenges for increasing a network lifespan; this is why effective routing protocols must be created for the MNs. This research focuses on improving energy utilization of WSNs by designing GA-based routing protocols. The selection, crossover, and mutation rules of the evolutionary strategy are integrated into the proposed protocol to dynamically find conditions leading to the occurrence of event-driven routing paths with minimum energy consumption and optimal load distribution. Therefore, the proposed work applies node clustering, adaptive data aggregation, and multi-objective optimization to improve energy usage while preserving the data accuracy and network connectivity. The violent and flexibility of the proposed GA-based protocol in contrast to LEACH and Power-Efficient GA-based Protocols is confirmed by using simulation outcomes in terms energy utilization, packet delivery tendency and network durability. The minutely details of the protocol implementation, ability to handle node mobility and sudden change of traffic loads also shows the stability of the proposed protocol. This research not only helps to development of sustainable WSN applications in field but open up the future path for step further enhancement of intelligent routing approach and optimum resource usage for next generation Wireless sensors networks.