Firefly and Aquila Optimization Based Clustering and Routing in IoT Assisted Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a substantial part of the Internet of Things (IoT) utilized for cluster and routing path applications to monitor computational fields. The major challenge of the Firefly Algorithm (FA) is its sensitivity to parameters such as the attractiveness coefficient and the randomization factor. Selecting appropriate parameter values can be challenging and may require fine-tuning. The proposed method utilizes energy levels in the IoT to efficiently manage routing data transmission, enhancing information transfer through the cluster router. Aquila Optimization (AO) often involves trade-offs between different parameters. Improving one aspect may negatively impact another, and finding the right balance can be difficult. Although the proposed FA and AO method exhibit high performance compared to existing methods such as Black Widow Optimization (BWO), Fixed Parameter Tractability (FPT), and Multipath Link Routing Protocol (MLRP). The proposed FA and AO method yield high results, achieving a Network Lifetime 1900 rounds, Packet Delivery Ratio (PDR) of 0.98, and an End-To-End Delay (ETED) of 0.80 ms. These outcomes outperform existing methods in the context of WSNs.