Mobile crowdsensing (MCS) has been applied for signal map construction in smart city. MCS leverages the mobility of users and the sensors embedded in mobile phones to collect and transfer sensing data. However, it is still costly for MCS to cover large-scale regions. Accordingly, data recovery algorithms are proposed, which allow participants to collect only few signal data and infer the rest by leveraging spatial-temporal correlation of signals. However, existing work only considered the temporal and 2-D spatial correlation in the plane, while the altitude dimension is not exploited. In this paper, we give an attempt to exploit the 3-D spatial-temporal correlation of signals to infer missing data and reconstruct ground signal map, where UAVs can be used to collect signals from the air. Two UAV-assisted ground signal map construction schemes are proposed based on propagation loss (PL) and convolution neural network (CNN). To further reduce the number of aerial samples required and reduce the cost incurred by UAV, a random-active sampling strategy is proposed to select more valuable signals in our work. Extensive simulations are performed which show that the proposed framework and schemes perform well under extremely high missing rate situations and outperform pure ground-based data recovery schemes. In addition, experiments for both indoor and outdoor are conducted to further verify the effectiveness of the proposed schemes.

With the dawn of 5G network, a new set of requirements for site spectrum monitoring, location-based services (LBS), network construction, and cellular planning are emerging, all of which are relying on fine-grained signal map. Although with significant importance, the traditional signal map construction, e.g., through full site survey, could be time-consuming and labor-intensive as the signal varies frequently over time and the accuracy requirement grows rapidly with the emergence of new applications. The state-of-the arts usually employ crowdsourcing scheme and matrix completion algorithm to solve the dilemma. However, the crowdsourcing scheme usually suffers from uneven distributed and inadequate participants, while the matrix completion methods do not take the specific signal map features into account, thus suffering from sub-optimal recovery results. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with smaller cost and propose a GAN-based active signal map reconstruction method (GSMAC). Our method is mainly innovative in two parts: GSMC, GAN-based signal map construction, and ACS, an active crowdsourcing scheme. Specifically, GSMC can effectively update the signal map with only a small number of observations while also fully using the incomplete historical signals to effectively update the signal map online. Meanwhile, ACS consists of a reinforce learning-based active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction, which further reduces the measurement cost to minimum. The simulation results and real implemented data driven experiments demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

Signal map is of great importance, especially in the dawn of 5G network, for site spectrum monitoring, location-based services (LBS), network construction, and cellular planning. Despite its significance, the traditional signal map construction, e.g., through full site survey, could be time-consuming and labor-intensive as the signal varies frequently over time and the accuracy requirement grows rapidly with the emergence of new applications. Even with crowdsourcing scheme, the participants tend to be unevenly distributed in space while the encouragement budgets for the participants could be far from enough to collect adequate high-quality measurements. Therefore, the signal map constructed by crowdsourcing is often sparse and incomplete. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with minimum cost and propose an auto-encoder-based active signal map reconstruction method (AER). Our method is mainly innovative in three parts. Firstly, AER can effectively update the signal map with only a small number of observations while also fully using the incomplete historical signals to effectively update the signal map online. Secondly, AER consists of an active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction, which further reduces the measurement cost to a large extent. Thirdly, to cope with the measurement dynamics, we give a new signal map model describing not only the signal strength but also the signal dynamics, based on which an advanced AER algorithm is proposed. The simulation results demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

Eddy current thermography (ECT) as one of the emerging nondestructive testing and evaluation techniques has been used for defects detection in critical components, e.g., fatigue cracks in turbine blades, bond wire lift-off in IGBT modules, lack of fusion in welded parts, etc. However, in fast inspection using the early thermal response, the thin eddy current penetration depth (skin depth) of ferromagnetic materials limits ECT's capability of detecting subsurface defects. In order to increase the detectable depth range, this paper proposes a dc-biased magnetization based ECT (DCMECT) technique. Based on the nonlinear magnetic permeability in ferromagnetic material, DCMECT can increase the thermal contrast between the defective and sound areas by the enhanced permeability distortion in the skin-depth layer. Specifically, the influences of dc-biased magnetization direction and intensity on the thermal responses (of the defective and sound areas) and their thermal contrast are investigated. Results show that the dc-biased magnetization direction has the strongest influence on the thermal response when it is parallel to the ac magnetization direction generated by the coil. Both the thermal responses of defective and sound areas decrease with the magnetization intensity increasing. Whereas, the thermal contrast between two areas increases with the magnetization intensity, which presents the enhanced defect detectability of DCMECT. The proposed technique can detect the subsurface defect with a buried depth up to 6 mm.