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

生物体通过其基因调控网络（GRNs）的优化结构来适应外部扰动。从长远来看，GRN的状态转换网络收敛到一组吸引子，使生物体对基因的去除或功能损伤具有弹性。在无线传感器网络（WSN）中，这样的吸引子是指一组传感器作为汇聚节点，用于多跳发送的数据包。该项目将这种基于吸引子的基因组鲁棒性映射到无线传感器网络上，以推断最佳拓扑结构和路由策略，从而减轻传感器故障和嘈杂的无线信道。这是通过进行计算机基因？击倒？通过模拟从样本GRNs中去除基因的功能来进行实验，以了解吸引子状态空间的动态。这些信息接下来用于设计WSN拓扑和路由协议，这些拓扑和路由协议对网络的不确定性、节点故障和妥协具有弹性。本计画的目的在于设计一个强健的无线传感器网路，以确保在既定的路由策略下，最大化封包成功传输的机率。指导原则是遵循自然？设计简单规则的步骤（即，路由算法），其保证优化的WSN拓扑上的最大效率。它还开发了创新的基于网络科学的工具，并提供了对GRNs和WSNs相互作用的见解，激发了工程系统的新设计（即WSNs的容错拓扑结构）。验证和测试完成真实的生活的无线传感器网络测试床。研究成果将通过出版物传播，并可用于设计新的研究生课程。

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.