EAGER: BISON: Bio-Inspired Approach to Robust Wireless Sensor Networking

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 experiments in silico gene 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.

This project is funded by National Science Foundation under Grant No. CNS-1355505.

Faculty

PhD Students

  • Armita Abedijaberi
  • Mayank Raj

Collaborators

  • Prof. Preetam Ghosh

 

The project is being executed in joint collaboration with researchers from CReWMaN Lab at Missouri University of Science and Technology and Biological Network Lab at Virginia Commonwealth University.

At CReWMaN lab we have been focusing on robust deployment of wireless sensor networks (WSNs) using gene regulatory networks (GRNs). In particular, we are trying to exploit the biological gene structures to deploy what is called bio-inspired WSNs. We believe that such a bio-inspired WSNs will be robust, have low latency, energy efficient, and resilient to node and link failures as it inherits many of the structural properties of the gene structures. 

The core research at Biological Networks Lab at VCU involves identification of gene regulatory network topologies which serve as efficient communication structures for bio-inspired WSNs. In this regard, we have focused on two aspects of the GRN topology identification: (i) reverse engineering algorithms to predict GRN topologies from micro array datasets and (ii) Growth algorithms predicting GRN topologies based on the popular preferential attachment models.