SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Abstract In the case of new technology application, cognitive radio network (CRN) addresses bandwidth shortfall and fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity resources mobility. this paper, we present a reconfigurable CRN-based cross-layer routing protocol purpose increasing performance optimizing data transfer in networks. Recently developed spotted hyena optimizer (SHO) is used tuning hyperparameters machine-learning models. system produces distributor built number tasks, such as load balance, quarter sensing development path machine learning. proposed technique sensitive traffic charges, well series other metrics interference (2bps/Hz/W average). tests are performed classic models that demonstrate residual energy strength resistant scalability resource.