Spider Wasp Optimization for Effective IoT Task Placement in Cloud-Fog Networks

Effective task placement algorithms in cloud-fog networks are required due to the substantial rise in data generated by the proliferation of Internet of Things (IoT) devices. This research introduces a unique Spider Wasp Optimization (SWO)-based method for IoT task allocation that improves performance and resource usage in cloud-fog situations. The SWO method is utilized to dynamically distribute IoT workloads between cloud and fog nodes, guaranteeing optimal load distribution and low latency. It is inspired by the hunting and nesting habits of spider wasps. To evaluate the performance of the suggested method, to carry out extensive simulations and compared them with conventional optimization methods. Our findings show that SWO greatly increases network resource management, decreases total execution time, and increases task placement efficiency. This work demonstrates how bio-inspired algorithms, such as SWO, can be used to tackle challenging IoT task placement problems in cloudfog networks, opening the door to more resilient and scalable IoT ecosystems.