Abstract By connecting a vast array of objects and enabling remote as well as global control over them, wireless sensor networks (WSN) and internet of things (IoT) have drastically altered the world in an advanced and effective manner. Making high precision on the real-time observations is the primary goal of implementing WSN-based applications, but it is very difficult to achieve due to the sensors’ limited computing power when operating in restricted environments, a large volume of high-speed, heterogeneous, and resource-limited data, including energy, memory, computation speed, quickly-changing data, and bandwidth. The IoT is a cutting-edge technology that is now a necessary component of smart cities. Sensor nodes must overcome resource constraints in order to carry out the improved routing procedure. Therefore, a hybrid heuristic method utilizing the osprey optimization algorithm (OOA) and barnacles mating optimizer (BMO) is suggested in this work in order to improve the processing of IoT-based WSN nodes. This algorithm is termed the hybrid barnacles mating with osprey optimization algorithm (Hy-BMOO) algorithm. In order to choose the best path for network transmission, this hybrid method examines the node properties. Consequently, the multi-objective function, which includes some constraints to guarantee system efficiency, is formulated with the aid of the optimum routing. Lastly, a comparison with certain current techniques and an analysis of the suggested model using specific metrics are conducted. As a result, thorough research reveals that it obtains encouraging outcomes in determining the best course for improved communication. Subjects Abbreviations - IoT: - Internet of things - WSN: - Wireless sensor networks - BMO: - Barnacles mating optimization - OOA: - Osprey optimization algorithm - Hy-BMOO: - Hybrid barnacles mating with osprey optimization - GA: - Genetic algorithm - PSO: - Particle swarm optimization - RNN: - Recurrent neural networks - DNN: - Deep neural networks -