Exploiting Gene Regulatory Networks for Robust Wireless Sensor Networking

Abstract

Gene Regulatory Networks (GRNs) represent the interactions of genes in living organisms, which have evolved over millions of years to provide a near-optimal structure for rapid adaptation to the environment. On the other hand, robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their topology and how quickly the network can recover from node and link failures. This article proposes a novel approach to design robust WSNs by exploiting GRNs. Specifically, we build bio-inspired WSNs based on the topology of GRNs. Our approach embeds the physical communication graph of the WSN into the GRN graph under the optimization criterion of minimizing the interference between different nodes. Furthermore, we propose an algorithm to identify data collection points (i.e., sinks) and improve robustness by maximizing the expansion of the network. Through an analytical evaluation, we show that our bio-inspired graph embedding approach leads to robust WSNs which preserve the structural properties of GRNs.