Flexible Topological Control for Underwater Optical Wireless Sensor Networks

Underwater Optical Wireless Sensor Network (UOWSN) is a promising technology as it can achieve high-speed communication in underwater environment. However, affected by the uncertainty of complex underwater environment, the network topology of UOWSN is highly dynamic, making it difficult to quantify flexibility or further optimize the topological structure. In this paper, we propose a flexibility-based network topology evaluation model (FEM) for UOWSNs. Then, a reinforcement learning model, termed FEM-DRL, for optimizing the network topology based on FEM is developed, which enables UOWSN to maintain an optimal topology when working in harsh underwater environments. Simulation results demonstrate that the proposed method can significantly improve the network flexibility and reduces the time cost for constructing network topology by 41.8% compared with baseline algorithms. Test-bed experiments verify the applicability and effectiveness in practical applications for detecting emergent events.