论文标题
物联网网络的可靠性和电池寿命改善：挑战和AI驱动解决方案
Reliability and Battery Lifetime Improvement for IoT Networks: Challenges and AI-powered solutions
论文作者
论文摘要
要实现一个智能的网络社会，为事物（也称为物联网）的事物（物联网）实现低成本的低成能连接至关重要。尽管现有的无线访问网络需要集中的信号来管理网络资源，但由于这种信号传导的能源消耗以及IoT设备数量的预期增加，这种方法对后代的无线网络的兴趣较小。然后，在这项工作中，我们研究了利用机器学习以进行物联网通信的分布式控制。为此，首先，我们研究适用于资源约束的物联网通信的低复合学习方案。然后，我们提出了一种轻量级学习方案，该方案使IoT设备能够将其通信参数调整到环境中。此外，我们研究了呈现集中控制方案的性能的分析表达式，以适应物联网设备的通信参数，并将结果与拟议的分布式学习方法的结果进行比较。模拟结果证实，通过利用拟议的学习方法，可以显着提高物联网通信的可靠性和能源效率。
Towards realizing an intelligent networked society, enabling low-cost low-energy connectivity for things, also known as Internet of Things (IoT), is of crucial importance. While the existing wireless access networks require centralized signaling for managing network resources, this approach is of less interest for future generations of wireless networks due to the energy consumption in such signaling and the expected increase in the number of IoT devices. Then, in this work we investigate leveraging machine learning for distributed control of IoT communications. Towards this end, first we investigate low-complex learning schemes which are applicable to resource-constrained IoT communications. Then, we propose a lightweight learning scheme which enables the IoT devices to adapt their communication parameters to the environment. Further, we investigate analytical expressions presenting performance of a centralized control scheme for adapting communication parameters of IoT devices, and compare the results with the results from the proposed distributed learning approach. The simulation results confirm that the reliability and energy efficiency of IoT communications could be significantly improved by leveraging the proposed learning approach.
