The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very high, and it brings to the mind the analogy to the human brain and nervous system, where a highly simplified scheme of information delivery is applied. Motivated by this similarity, in this paper, we study the possibility of the application of various transmission profiles in order to optimize the overall energy consumption in such dense wireless networks. The transmission profile specifies the radio access and energy consumption of the wireless transceiver (network node), and is characterized by the tuple of parameters, e.g., the total transmit power or minimal required signal-to-noise ratio (SNR). In the considered multi-hop network, we assume that each node can be set to the most promising transmission profile to achieve some predefined goals, such as (sensor) network reliability or transmission energy efficiency. We have proposed the new graph-based routing algorithm in such a dense wireless network, where total power consumption of message delivery is minimized by multihop and multimode transmission. The theoretical definition of the prospective transmission schemes is supported by the analysis of the results of the simulation experiments.
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http://dx.doi.org/10.3390/s21010134 | DOI Listing |
ACS Nano
January 2025
School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China.
Neural-electronic interfaces through delivering electroceuticals to lesions and modulating pathological endogenous electrical environments offer exciting opportunities to treat drug-refractory neurological disorders. Such an interface should ideally be compatible with the neural tissue and aggressive biofluid environment. Unfortunately, no interface specifically designed for the biofluid environments is available so far; instead, simply stacking an encapsulation layer on silicon-based substrates makes them susceptible to biofluid leakage, device malfunction, and foreign-body reactions.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Electro-Optical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan.
In this paper, we demonstrated a novel bidirectional high-speed transmission system integrating a free-space optical (FSO) communication with a 5G wireless link, utilizing a high-power erbium-doped fibre amplifier (EDFA) for enhanced loss compensation. The system supports downlink rates of 1-Gb/s/4.5-GHz and 10-Gb/s at 24-GHz and 39-GHz, and an uplink rate of 10-Gb/s/28-GHz.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Oak Ridge Institute of Science and Education (ORISE), Oak Ridge, TN 37831, USA.
This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor-capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline AlO substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz.
View Article and Find Full Text PDFOne avenue to better understand brain evolution is to map molecular patterns of evolutionary changes in neuronal cell types across entire nervous systems of distantly related species. Generating whole-animal single-cell transcriptomes of three nematode species from the genus, we observed a remarkable stability of neuronal cell type identities over more than 45 million years of evolution. Conserved patterns of combinatorial expression of homeodomain transcription factors are among the best classifiers of homologous neuron classes.
View Article and Find Full Text PDFACS Sens
December 2024
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei Province 430074, China.
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