Electrical conductivity is one of several outstanding features of graphene–polymer nanocomposites, but calculations of this property require the intricate features of the underlying conduction processes to be accounted for. To this end, a novel Monte Carlo method was developed. We first established a randomly distributed graphene nanoplatelet (GNP) network. Then, based on the tunneling effect, the contact conductance between the GNPs was calculated. Coated surfaces (CSs) were next set up to calculate the current flow from the GNPs to the polymer. Using the equipotential approximation, the potentials of the GNPs and CSs met Kirchhoff’s current law, and, based on Laplace equation, the potential of the CSs was obtained from the potential of the GNP by the walk-on-spheres (WoS) method. As such, the potentials of all GNPs were obtained, and the electrical conductivity of the GNP polymer composites was calculated. The barrier heights, polymer conductivity, diameter and thickness of the GNP determining the electrical conductivity of composites were studied in this model. The calculated conductivity and percolation threshold were shown to agree with experimental data.
In recent years, a typical representation of the next-generation Internet architecture, named data networking (NDN), and a critical form of the underwater Internet of Things (IoT), underwater acoustic sensor networks (UASNs), have attracted widespread attention in academia. Meanwhile, since the battery energy of the sensor node is limited and the battery is difficult to replace or recharge in underwater environments, extending the networks' lifetime has become a key issue in UASNs. In this paper, we try to deploy a UASN on NDN architecture and explore the energy consumption of the NDN-based UASN under shallow water and deep water conditions based on the relay network topology. A simulation is carried out to compare the delay performance of NDN-based and IP-based UASNs and validate the result. It is believed that the study could provide a theoretical criterion for the selection of the direct or relay path to optimize energy consumption in the future deployment of NDN-based UASNs.INDEX TERMS Energy consumption, named data networking, underwater acoustic sensor networks.
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