EAGER: Collaborative Research: Improving the efficiency of Wireless Sensor Networks using principles of Genomic Robustness

生物通过其基因调控网络（grn）的优化结构来适应外部扰动。从长期来看，GRN的状态转换网络收敛于一组吸引子，这些吸引子使生物体能够适应基因的移除或功能损伤。在无线传感器网络（WSN）中，这种吸引器指的是一组传感器，它们作为多跳发送的数据包的接收节点。该项目将这种基于吸引子的基因组鲁棒性映射到wsn上，以推断出减轻传感器故障和无线信道噪声的最佳拓扑和路由策略。这是通过在硅中进行基因敲除来实现的。实验通过模拟基因从样本grn的功能性去除，了解吸引子状态空间的动力学。这些信息随后用于设计WSN拓扑和路由协议，以适应网络不确定性、节点故障和妥协。本课题致力于设计鲁棒WSN中传感器间的最佳布线规则，以保证在给定路由策略下数据包成功传输的最大概率。指导原则是遵循自然？在设计简单规则（即路由算法）方面迈出了一步，这些规则保证了优化后的WSN拓扑的最大效率。它还开发了基于网络科学的创新工具，并提供了对grn和wsn相互作用的见解，从而激发了工程系统的新设计（即wsn的容错拓扑）。验证和测试是在实际的无线传感器网络测试台上完成的。除了允许设计新的研究生课程外，研究成果将通过出版物传播。

Organisms adapt to external perturbations through the optimized structure of their gene regulatory networks (GRNs). In the long-term, the state transition network of a GRN converges to a set of attractors that make the organism resilient to removal or functional impairment of genes. In wireless sensor networks (WSN), such attractors refer to a group of sensors serving as sink nodes for packets sent over multiple hops. This project maps such attractor based genomic robustness onto WSNs to infer optimal topologies and routing strategies that mitigate both sensor failure and a noisy wireless channel. This is being achieved by conducting in silico gene ?knock-down? experiments by simulating the functional removal of a gene from sample GRNs, to understand the dynamics of the attractor state space. This information is next used to design WSN topologies and routing protocols that are resilient to network uncertainty, node breakdown and compromise. This project pursues the design of optimal wiring rules between sensors in a robust WSN that guarantees maximum probability of successful packet transmission under a given routing strategy. The guiding principle is to follow nature?s foot-steps in designing simple rules (i.e., routing algorithms) that guarantee maximum efficiency over an optimized WSN topology. It also develops innovative network-science based tools, and provides insights into the interplay of GRNs and WSNs that inspire new designs for engineered systems (i.e. fault-tolerant topologies for WSNs). Validation and testing are accomplished on real life WSN testbeds. Research results will be disseminated through publications, besides allowing for the design of new graduate-level courses.