The emerging needs of innovative services in different areas led to the development of advanced intelligent systems using the heterogeneous technologies, devised by Internet of Things (IoT). IoT focuses on integrating the networks to facilitate smooth services to the humans. The interface between mobility patterns and the routing protocols contributes significantly to alter the performance of network. This paper proposes routing protocol based on Sunflower based grey wolf optimization (SFG) algorithm for improving the network lifetime. The first step is the simulation of IoT and then, the multipath routing is initiated in the IoT network. The SFG algorithm selects the best path from the multipath available for routing, based on Context awareness, Network lifetime, Residual Energy, Trust, and Delay. Finally, the multipath routing takes place in the IoT network through optimal routing path selected using the proposed SFG algorithm. The proposed SFG algorithm is designed by integrating sun flower optimization (SFO) and the grey wolf optimizer (GWO) such that the optimal routes are selected. The proposed SFG outperformed other methods with minimal delay of 0.779 s, maximal energy of 0.203 J, maximal network lifetime of 98.039%, and maximal throughput of 47.368%, respectively.

不同领域对创新服务的新兴需求导致了使用物联网（IoT）设计的异构技术开发高级智能系统。物联网专注于集成网络以促进对人类的顺畅服务。移动性模式和路由协议之间的接口对更改网络性能做出了重要贡献。提出了一种基于向日葵的灰狼优化（SFG）算法的路由协议，以提高网络寿命。第一步是物联网的仿真，然后在物联网网络中启动多路径路由。SFG算法根据上下文感知，网络生存期，剩余能量，信任和延迟从多路径中选择最佳路径进行路由。最后，多路径路由是通过使用建议的SFG算法选择的最佳路由路径在IoT网络中进行的。提出的SFG算法是通过整合太阳花优化（SFO）和灰狼优化器（GWO）来设计的，从而选择了最佳路线。所提出的SFG的性能优于其他方法，其最小延迟为0.779 s，最大能量为0.203 J，最大网络寿命为98.039％，最大吞吐量为47.368％。