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Energy-Aware Adaptive Topology Control for UOWSNs
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DOI：10.1109/tmc.2026.3693790.png)
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Abstract
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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. Additionally, node mobility and energy constraints pose significant challenges to reliable communication. In this paper, we propose a mobility-aware and energy-efficient flexibility-based network topology evaluation model (ME-FEM) for UOWSNs. Then, a reinforcement learning model, termed ME-FEM-DRL, for optimizing the network topology based on ME-FEM is developed, which enables UOWSN to maintain an optimal topology when working in harsh underwater environments. Theoretical analysis proves the NP-hardness of the optimization problem and demonstrates that our algorithm achieves an approximation ratio of <inline-formula><tex-math notation="LaTeX">$O(\log N)$</tex-math></inline-formula> with optimal parameter boundaries. Simulation results demonstrate that the proposed method can significantly improve the network flexibility. Compared with the five baseline algorithms in simulations, ME-FEM-DRL reduces normalized topology optimization time cost by 64% and extends network lifetime by 95% on average. Test-bed experiments verify the applicability and effectiveness in practical applications for detecting emergent events.
Keywords:
Underwater optical wireless sensor networks
mobility-aware networking
energy efficiency
topology optimization
deep reinforcement learning
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