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 the 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 paper.

翻译：严酷的环境给网络战略带来了一套独特的挑战。在这种情况下,尚未对网络资源和长期无人照料的维护对环境的影响进行充分调查。为了应对这些挑战,我们提议一个灵活的适应性资源管理框架,纳入环境意识功能。我们特别提议一个新的网络架构,并针对传统的网络组成部分引入新的功能。拟议架构的新颖之处包括一个基于深层次学习的环境资源预测模块和一个自我组织的服务管理模块。具体地说,各种环境条件下的现有网络资源是通过使用预测模块预测的。然后,根据预测,开发一种面向环境的资源分配方法,以优化系统效用。为了展示拟议新功能的效力和效率,我们在案例研究中通过实验研究该方法。最后,我们介绍了在严酷环境中资源管理的若干有希望的方向,从本文中可以扩展。