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The wireless electromagnetic method (WEM), a new method combining geophysics and radio physics, can be used for seismic monitoring, ionospheric structure exploration and electrical structure detection within a depth of 10m underground. As the wavelength of an electromagnetic wave can be equal the circumference of the earth, the influence of ionosphere and earth curvature on electromagnetic waves shall be considered. Considering that the earth is a multi-layer geoelectric medium, a multi-layer “Earth-ionosphere” model is established to simulate the electromagnetic field more accurately in the actual complex underground structure. By combining the W.K.B method and the impedance recursion method, we initially obtained the response formula under such a model, calculated the Schumann Resonance within the low frequency range of 1-30Hz, and verified the correctness of the algorithm proposed by this paper; we subsequently obtained the propagation characteristics and the frequency characteristics of the electromagnetic field under different electrical structures by simulation and calculation, and analyzed the propagation law of the electromagnetic field at different times and at different equivalent heights of the ionosphere. The results revealed that the influence of the ionosphere and the earth curvature must be accounted for in large-scale deep detection, and different underground structures and the equivalent heights of ionosphere at different times may lead to differences in the value of the electromagnetic field. Therefore, the multi-layered spherical “Earth-ionosphere” model can better meet the requirements of high-power WEM for the forward calculation of underground complex electrical structure detection and assist the refined exploration by such a method.

Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

This article unveils the importance of statistical quality of service (QoS) for resource allocation in a two-hop network. Particularly, an access point (AP) serves multiple IoT devices for information transfer with the assistance of the energy harvesting (EH) relaying. To explore the maximum constant data arrival metric, we aim to maximize the effective capacity (EC) under specified QoS requirements. Also, the statistical QoS inspired resource allocation policies are investigated for half/full duplex (HD/FD) modes, respectively, to jointly optimize power allocation and power splitting (PS) ratio. To solve the formulated problem, we first derive the closed-form solution of the optimal power allocation at the AP and the PS ratio. To gain more insights, we further derive the boundary conditions of optimal power allocation and PS ratio. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

In this paper, the load time-series measured from 2016 to 2018 in Qingdao is investigated in order to make a prediction more accurately by using an artificial neural network model combined with some regular and irregular features. The results of spectral analysis show that several periodic variations in diurnal, semidiurnal and weekly frequencies are prominent, and considered as critical parts of predictior variables of the training and test sets in this forecasting model. However, a significant decline of load happens during the national statutory festivals in China, and is more obvious with the longer holidays, which should be taken into account as abnormal conditions. Moreover, both temperatuer and hummity filtered out by mutual information method are also added as two essential weather factors to improve the prediction accuracy, especially in the hot summer. Finally, the comparative results of five different experiments in term of mean absolute percent error show that the forecasting model combined with all periodic and non-periodic factors outperforms the others with one single or a few of factors. This multifactorial model, taking fully account of internal charateristics of load data and external important influences, is proved more suitable to predict the load trend in Qingdao.