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

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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.