Recent development in wireless communications has provided many reliable solutions to emergency response issues, especially in scenarios with dysfunctional or congested base stations. Prior studies on underwater emergency communications, however, remain under-studied, which poses a need for combining the merits of different underwater communication links (UCLs) and the manipulability of unmanned vehicles. To realize energy-efficient underwater emergency communications, we develop a novel underwater emergency communication network (UECN) assisted by multiple links, including underwater light, acoustic, and radio frequency links, and autonomous underwater vehicles (AUVs) for collecting and transmitting underwater emergency data. First, we determine the optimal emergency response mode for an underwater sensor node (USN) using greedy search and reinforcement learning (RL), so that isolated USNs (I-USNs) can be identified. Second, according to the distribution of I-USNs, we dispatch AUVs to assist I-USNs in data transmission, i.e., jointly optimizing the locations and controls of AUVs to minimize the time for data collection and underwater movement. Finally, an adaptive clustering-based multi-objective evolutionary algorithm is proposed to jointly optimize the number of AUVs and the transmit power of I-USNs, subject to a given set of constraints on transmit power, signal-to-interference-plus-noise ratios (SINRs), outage probabilities, and energy, which achieves the best tradeoff between the maximum emergency response time (ERT) and the total energy consumption (EC). Simulation results indicate that our proposed approach outperforms benchmark schemes in terms of energy efficiency (EE), contributing to underwater emergency communications.

翻译：无线通信领域的最新进展为应急响应问题提供了许多可靠的解决方案，尤其是在基站瘫痪或拥塞的场景中。然而，针对水下应急通信的研究仍相对不足，亟需融合不同水下通信链路（UCL）的优势以及无人载具的可操控性。为实现节能的水下应急通信，我们构建了一种新型水下应急通信网络（UECN），该网络借助多种链路（包括水下光链路、声链路和射频链路）以及自主水下航行器（AUV）来收集和传输水下应急数据。首先，我们采用贪婪搜索与强化学习（RL）确定水下传感器节点（USN）的最优应急响应模式，从而识别出孤立USN（I-USN）。其次，根据I-USN的分布情况，我们派遣AUV辅助I-USN进行数据传输，即联合优化AUV的位置与控制策略，以最小化数据采集与水下移动时间。最后，提出一种基于自适应聚类的多目标进化算法，在发射功率、信干噪比（SINR）、中断概率和能量等约束条件下，联合优化AUV数量与I-USN的发射功率，从而实现最大应急响应时间（ERT）与总能耗（EC）之间的最佳权衡。仿真结果表明，所提方法在能效（EE）方面优于基准方案，为水下应急通信做出了贡献。