The proliferation of advanced underwater technology and the emergence of various cloud services promote the horizon of cloud-based underwater acoustic sensor network (UASN). Sending end data to cloud for analysis is becoming a prominent trend, driving cloud computing as an indispensable computing paradigm. However, UASN bears tremendous burdens with respect to data collection from end to cloud, such as large transmission power consumption and high delay, which makes it difficult to meet the delay-sensitive and context-aware service requirements by using cloud computing alone. To this end, a two-level bidirectional data prediction model for end-edge-cloud orchestration is proposed in this article. The mobility and computing ability of edge elements are exploited to analyze and collect data. Edge elements predict the future data based on historical information and trend to decrease acoustic communication. Moreover, a data collection protocol with mobile edge elements is designed. With this protocol, computing paradigms are shifted from centralized cloud to distributed edge, and the differentiated capability of heterogeneous devices is exploited. After extensive experiments, the results show that the data collection cost is dramatically decreased while the bandwidth utilization is increased, which is critical for underwater acoustic communication. The proposed method and protocol strike a good balance between data accuracy and energy consumption for the new end-edge-cloud orchestrated system.

中文翻译：

---

用于端边云编排系统的基于双向预测的水下数据收集协议


先进水下技术的激增和各种云服务的出现推动了基于云的水声传感器网络（UASN）的发展。将终端数据发送到云端进行分析正在成为一种突出趋势，推动云计算成为不可或缺的计算范式。然而，UASN在从端到云的数据采集方面承受着巨大的负担，如传输功耗大、时延高等，仅依靠云计算很难满足时延敏感和上下文感知的业务需求。为此，本文提出了一种端-边-云编排的两级双向数据预测模型。利用边缘元件的移动性和计算能力来分析和收集数据。边缘元件根据历史信息和减少声学通信的趋势来预测未来数据。此外，还设计了具有移动边缘元素的数据收集协议。通过该协议，计算范式从集中式云转移到分布式边缘，并利用异构设备的差异化能力。经过大量实验，结果表明，数据采集成本大幅降低，带宽利用率提高，这对于水声通信至关重要。所提出的方法和协议在新型端边云编排系统的数据准确性和能耗之间取得了良好的平衡。