基于水下光无线通信的移动自主水下航行器集成发现与状态感知服务：建模与性能分析
Integrated Discovery and State-Aware Servicing for Mobile AUVs With UOWC: Modeling and Performance Analysis
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中文总结 AI 辅助
研究基于UWOC的移动AUV为传感器节点网络进行联合无线信息与功率传输，开发集成任务级框架，含基于SNR的节点发现模型及SA - OPS调度框架，仿真表明其能改善AUV能耗与网络能量健康权衡，充电阈值有实用近似方法。
中文摘要 AI 辅助
水下无线光通信（UWOC）是实现高通量海底网络的一项关键技术，但其长期部署受水下节点有限能量预算的限制。为应对这一挑战，本文研究了一种移动系统，其中自主水下航行器（AUV）为随机分布的传感器节点网络执行联合无线信息传输（WIT）和无线功率传输（WPT）。本文开发了一个集成任务级框架，将随机节点发现与状态感知服务相结合。首先，基于信噪比（SNR）分析提出了节点发现的分析模型，得出包括发现距离概率分布在内的性能指标。其次，引入了基于阈值的调度框架，即状态感知最优点服务（SA - OPS），它根据节点的实时能量状态从三种操作中选择一种：抢先充电、通信后充电或仅通信。仿真和多准则决策分析表明，在所考虑的假设和参数范围内，SA - OPS相对于采用的基线策略可以改善AUV能量消耗与全网络能量健康之间的权衡。结果还表明，所选充电阈值可以通过一个简单的状态相关启发式方法近似，为水下网络中的自主能量补充提供了实用指南。
英文摘要
Underwater wireless optical communication (UWOC) is an enabling technology for high-throughput subsea networks, yet its long-term deployment is constrained by the finite energy budget of underwater nodes. To address this challenge, we investigate a mobile system wherein an autonomous underwater vehicle (AUV) performs joint wireless information transfer (WIT) and wireless power transfer (WPT) for a network of randomly distributed sensor nodes. This paper develops \textcolor{blue}{an integrated mission-level framework} that combines stochastic node discovery with state-aware servicing. First, we present an analytical model for node discovery based on a signal-to-noise ratio (SNR) analysis, deriving performance metrics that include the probability distribution of the discovery distance. Second, we introduce \textcolor{blue}{a threshold-based scheduling framework}, termed State-Aware Optimal Point Servicing (SA-OPS), which \textcolor{blue}{selects one of three actions according to the node's real-time energy state: preemptive charging, communication followed by charging, or communication only.} Simulations and multi-criteria decision analysis show that, \textcolor{blue}{under the considered assumptions and parameter ranges}, SA-OPS can improve the tradeoff between AUV energy expenditure and network-wide energy health relative to the adopted baseline strategies. The results also indicate that the selected charging threshold can be approximated by \textcolor{blue}{a simple state-dependent heuristic}, providing a practical guideline for autonomous energy replenishment in underwater networks.
发表机构
- King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。
