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

EAGER：协作研究：利用基因组稳健性原理提高无线传感器网络的效率

• 批准号: 1049652
• 负责人: Sajal Das
• 金额: $ 11.73万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049652&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Improving; efficiency

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.

生物通过其基因调控网络(GRN)的优化结构来适应外界的干扰。从长远来看，GRN的状态转移网络收敛到一组吸引子，使有机体对基因的移除或功能损伤具有弹性。在无线传感器网络(WSN)中，这样的吸引子指的是一组传感器，充当通过多跳发送的分组的汇聚节点。该项目将这种基于吸引子的基因组健壮性映射到无线传感器网络上，以推断出最优的拓扑和路由策略，从而同时减轻传感器故障和噪声无线信道的影响。这是通过在硅基因中进行传导来实现的？击倒？实验通过模拟从样本GRN中功能移除一个基因，来了解吸引子状态空间的动力学。这些信息接下来将用于设计WSN拓扑和路由协议，这些协议对网络不确定性、节点故障和危害具有弹性。该项目致力于设计健壮的无线传感器网络中传感器之间的最优布线规则，以保证在给定的路由策略下实现最大的成功分组传输概率。指导原则是遵循自然S的脚步来设计简单的规则(即路由算法)，以确保在优化的无线传感器网络拓扑上实现最大效率。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，从而启发了工程系统的新设计(即WSN的容错拓扑)。验证和测试是在真实的无线传感器网络测试床上完成的。除了允许设计新的研究生课程外，研究成果还将通过出版物传播。