  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.
