Abstract
Wireless sensor networks (WSNs) form a critical component in modern computing applications; given their size, ability to process and communicate information, and to sense stimuli, they are a promising part of the Internet of Things. However, they are also plagued by reliability and node failure problems. Here we address these problems by using the Gene Regulatory Networks (GRNs) of the organism Escherichia coli—believed to be robust against signaling disruptions, such as gene failures—to study the transmission properties of randomly-generated WSNs and transmission structures derived from these genetic networks. Selection of sink nodes is crucial to the performance of these networks; here we introduce four sink-node selection techniques: two motif-based, an attractor based and a highest degree-based approach and perform comprehensive simulations to assess their performance. Specifically, we use NS-2 simulations to evaluate the packet transmission robustness properties of such GRN-derived communication structures as against typical randomly deployed sensor network topologies under varying channel loss models. Packet receipt rates are compared among these networks, which are shown to be higher using GRNs for the communication structure, rather than randomly generated WSNs. We also evaluate the performance of communication structures derived from existing biological network generation models to assess their applicability in providing robust communication. This work paves the way for future development of fault-tolerant and robust WSN deployment and routing algorithms based on the inherent signal transmission robustness properties of the gene regulatory network topologies.
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This work was supported by grant number NSF-1143737, and the US Army’s Environmental Quality and Installations 6.1 basic research program. The Chief of Engineers approved this material for publication.
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Kamapantula, B.K., Abdelzaher, A., Ghosh, P. et al. Leveraging the robustness of genetic networks: a case study on bio-inspired wireless sensor network topologies. J Ambient Intell Human Comput 5, 323–339 (2014). https://doi.org/10.1007/s12652-013-0180-0
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DOI: https://doi.org/10.1007/s12652-013-0180-0