Leveraging Bio-Inspired Metaheuristics for Effective Congestion Control in Wireless Sensor Networks

Wireless sensor networks (WSNs) are among the most limited wireless networks. Congestion-like events are very unwanted for such networks since they result in a considerable number of packet losses and repeated transmissions. This is because of the huge volume of traffic. As a consequence of this, it is better to control congestion from the very beginning of the process during what interaction is taking place. Utilizing a Bio-inspired Meta Heuristics strategy, this chapter has developed a congestion management strategy that is both energy-efficient and effective for clustered wireless sensor networks (WSNs). An application of the firefly method has been made in order to pick the node that is the most appropriate and has the least amount of congestion as the cluster head for the purpose of transmitting data to the sink node. It has also been described that the cluster creation phase is the period in which nodes equipped with sensors which are not cluster heads join the CH that is most suited to their requirements based on weighting functions. A number of different WSN situations are used to conduct an exhaustive evaluation of the approach. There are a number of evaluation metrics that are employed in order to evaluate the effectiveness of the suggested optimization-based congestion mitigation approach. Some of these measurements include throughput, network lifespan, and energy usage analysis. The Proposed technique Firefly-based Congestion Control (FFCC) is consistently better than Low-Energy Adaptive Clustering Hierarchy (LEACH), Congestion Control using Particle Swarm Optimization (CC-PSO), and the other two approaches in all cases. In contrast to LEACH and CC-PSO, the Proposed approach outperforms both. The proposed technique outperforms previous models in efficiency and dependability with an average accuracy of 95.67%.