Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 4; p. 1133
• Main Authors: Murakami, Masaya, Kominami, Daichi, Leibnitz, Kenji, Murata, Masayuki
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.04.2018; MDPI
• Subjects: Brain; brain networks; Cerebral cortex; Communication; Computer Communication Networks; Computer simulation; Construction methods; Humans; Internet; Internet of Things; Links; Network topologies; Remote sensors; Test procedures; Virtual networks; Wireless sensor networks; virtual networks; wireless sensor networks; Internet of Things; brain networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18041133

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.