Principles of genomic robustness inspire fault-tolerant WSN topologies: A network science based case study

Wireless sensor networks (WSNs) are frameworks for modern pervasive computing infrastructures, and are often subject to operational difficulties, such as the inability to effectively mitigate signal noise or sensor failure. Natural systems, such as gene regulatory networks (GRNs), participate in similar information transport and are often subject to similar operational disruptions (noise, damage, etc.). Moreover, they self-adapt to maintain system function under adverse conditions. Using a PBN-type model valid in the operational and functional overlap between GRNs and WSNs, we study how attractors in the GRN-the target state of an evolving network-behave under selective gene or sensor failure. For “larger” networks, attractors are “robust”, in the sense that gene failures (or selective sensor failures in the WSN) conditionally increase their total number; the “distance” between initial states and their attractors (interpreted as the end-to-end packet delay) simultaneously decreases. Moreover, the number of attractors is conserved if the receiving sensor returns packets to the transmitting node; however, the distance to the attractors increases under similar conditions and sensor failures. Interpreting network state-transitions as packet transmission scenarios may allow for trade-offs between network topology and attractor robustness to be exploited to design novel fault-tolerant routing protocols, or other damage-mitigation strategies.