Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management
The harsh environment imposes a unique set of challenges on networking strategies. In such circumstances, the environmental impact on network resources and long-time unattended maintenance has not been well investigated yet. To address these challenges, we propose a flexible and adaptive resource management framework that incorporates environment awareness functionality. In particular, we propose a new network architecture and introduce the new functionalities against the traditional network components. The novelties of the proposed architecture include a deep-learning-based environment resource prediction module and a self-organized service management module. Specifically, the available network resource under various environmental conditions is predicted by using the prediction module. Then, based on the prediction, an environment-oriented resource allocation method is developed to optimize the system utility. To demonstrate the effectiveness and efficiency of the proposed new functionalities, we examine the method via an experiment in a case study. Finally, we introduce several promising directions of resource management in harsh environments that can be extended from this article.
Jiaqi Zou, Rui Liu, Chenwei Wang, Yuanhao Cui, Zixuan Zou, Songlin Sun, and Koichi Adachi. "Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management" IEEE Network (2022): 70-77. https://doi.org/10.1109/MNET.005.2100687