A gene regulatory network based framework for self-organization in mobile sensor networks

It is desirable to self-organize mobile sensor networks to form different yet suitable patterns to adapt to environment changes dynamically in uncertain environments. Inspired by biological morphogenesis which is guided by gene regulatory networks (GRNs), in this study, we propose a GRN-based approach to self-organization of mobile sensor networks in dynamic, uncertain environments. Instead of predefining the dynamics of the GRN model like other alternative studies, we aim to evolve the GRN framework to an appropriate structure automatically. Recently biological studies found out that network motifs are simple universal building blocks for most complex networks. Based on this study, the basic idea of the GRN-based approach is as follows: first, some predefined network motifs are employed as the basic building blocks, then an evolutionary algorithm is applied to evolve parameters and the structures of the GRN-based model based on these basic building blocks. Several simulation results have demonstrated that the proposed bio-inspired model is efficient for the self-organization of mobile sensor networks and robust to environmental changes in complex environments.