The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

中文翻译：

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转录调控网络拓扑在仿生网络中的应用：一项调查

边缘计算网络范式的出现使计算和存储资源远离数据中心，更靠近网络边缘，主要包括收集大量数据的异构物联网设备。与传统的以云为中心的范式相比，这种范式显着改善了网络延迟和带宽使用。然而，下一代网络仍然无法在动态且容易发生故障的环境中实现自适应、节能、及时的数据传输——这是生物网络所面临的优化挑战，因为数以百万计的年的演变。转录调控网络（TRN）是一种生物网络，其先天的拓扑鲁棒性是其底层图拓扑的函数。在本文中，我们调查了 TRN 的这些属性以及从中得出的指标，这些指标有助于设计智能网络协议和架构。然后，我们回顾了一系列关于利用 TRN 规定属性的仿生网络解决方案的文献。最后，我们提出了 TRN 特定方面的愿景，这可能会激发大规模社交和通信网络领域的未来研究方向。