Optimizing Energy-Efficient Routing in Wireless Sensor Networks via Genetic Algorithm for Enhanced Performance

Wireless Sensor Networks (WSNs) are employed in various fields like environment monitoring and smart cities in which well-defined routing with less energy consumption for path discovery is a major area of concern for longer network lifetime and better efficiency. This work focuses on energy efficiency in WSNs with an emphasis on routing optimization through Genetic Algorithms (GAs). The above approach therefore uses GAs to search for the best route plan that will help transfer data at high rates while, at the same time causing low energy utilization. Specifically, the routing paths are encoded as chromosomes, and crossover and mutation operations are performed on these chromosomes; the fitness estimation of the solutions is made based on the energy and network performance functions. The proposed GA-based routing protocol is analyzed and compared with LEACH and Direct Transmission and the congestion control and average energy consumption proves that the protocol has an improved network lifetime, less packet delivery ratio. A closer look at the results show that population size if increased and the mutant rate if optimised improves the performance of the algorithm. Moreover, there are numerous integration of GA with other Algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and so on. Thus, the obtained results reveal the effectiveness of the GA-based methods for designing efficient and power-aware routing algorithms in WSNs, which will help to design more effective and resource-saving networks in the future. It will be of future work to improve these algorithms and to investigate on how to apply it in dynamic and real-life conditions.