Advanced Broadband Communications Center (CCABA), Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain.
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Sensors (Basel). 2023 Jan 16;23(2):1043. doi: 10.3390/s23021043.
The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios.
向新一代 Beyond 5G（B5G）网络的演进不仅需要在传输技术方面进行创新，还需要采用更智能、更高效的操作方式，以满足预计在未来几十年内将成为网络资源高消耗者的用例。在不同的 B5G 用例中，数字孪生（DT）已被确定为一个关键的高带宽需求用例。创建和操作 DT 需要连续收集大量广泛分布的传感器遥测数据，这可能会使传输层不堪重负。因此，减少此类传输的遥测数据是智能用例操作的一个基本目标。此外，深度遥测数据分析，即异常检测，可以分层执行，以减少以集中方式执行此类分析所需的处理。在本文中，我们提出了一个智能管理系统，该系统由使用深度自动编码器（AE）进行遥测传感器数据分析的分层架构组成。该系统包含基于 AE 的方法，用于使用 AE 池（称为 AAC）自适应压缩遥测时间序列数据，以及用于单传感器（称为 SS-AD）和多传感器（称为 MS-AGD）流中的异常检测。使用实验遥测数据的数值结果表明，压缩比高达 64%，重建误差小于 1%，明显优于基准的最新方法。此外，对于单传感器和多传感器场景，都演示了快速准确的异常检测。最后，通过分布式 DT 场景的智能用例操作，可将传输网络容量资源减少 50%甚至更多。
