Data Aggregation Scheme Using Differential Evolution with Sailfish Optimization for Clustering and Routing in IoT

Internet of Things (IoT) facilitates connectivity in businesses and smart homes by integrating embedded technology, wireless sensor networks and data aggregation.Regular monitoring of energy usage in IoT networks is crucial due to the high energy consumption and delays in transmitting data to the Base Station (BS) by the sensor nodes.The most significant challenges in IoT include energy depletion and transmission delays.In this research, the proposed Differential Evolution with Sailfish Optimization (DESFO) model addresses large network handling, achieves maximum convergence rates, and reduces energy consumption.The Differential Evolution (DE) mutation and crossover operators enhance exploration capabilities, while SFO adaptive movement strategies improve the exploitation of the search space.Together, they achieve high convergence rates, prevent falling into local optima, provide iterative control and manage high-dimensional networks effectively.The DESFO method exhibits superior performance when compared to the existing methods, Firefly Optimization and Aquila Optimization (FF-AO), Fixed-Parameter Tractable Approximation Clustering (FPTAC), and Cluster based Reliable Data Aggregation-Sunflower Optimization (CRDA-SFO).The proposed DESFO method yields impressive results, achieving a Packet Delivery Ratio (PDR) of 96.12% at 250 nodes, a Delay of 3ms at 250node, Energy consumption of 12J at 250 respectively.