Rapid, accurate, and energy-efficient localization of underground sensor nodes remains a key challenge in wireless underground sensor networks (WUSNs) due to severe signal attenuation and complex subterranean conditions. In the meantime, Unmanned Aerial Vehicles (UAVs) can overcome accessibility constraints by enabling flexible coverage across diverse environments, including Urban, Suburban, Farm, and Rural scenarios. This paper presents a multi-objective reinforcement learning framework that combines a UAV-based MADDPG algorithm with the Prioritized Experience Replay (PER) method, leveraging multimodal localization of hard-to-reach underground sensor nodes. With coordinated UAV navigation, the framework further adopts a relative localization paradigm, where potential collisions of UAVs are resolved through a vertical-prioritized collision avoidance strategy. By fusing multimodal signals and benchmarking against the Cramér–Rao Lower Bound, the combination of the Received Signal Strength Indicator and the Time-Of-Arrival is identified as a practical trade-off. Building on this scheme, extensive simulations demonstrate that the proposed MADDPG+PER algorithm reduces the 90% CDF of the aggregated 3D Relative Positioning Error (3D-RPE) by 1.8%–22.8% relative to the MADDPG baseline, and by at least 6.8% when compared with non-learning heuristic baselines, across all investigated environments. Meanwhile, as the UAV team size increases from two to twenty, the mean flight distance drops by 85.4%–86.1% and the energy consumption drops by 84.3%–84.4% across all environments, whereas the 90% CDF of aggregated 3D-RPE decreases by 44.4%–47.5%. The results also show that the vertical-prioritized collision avoidance scheme not only improves the flight efficiency, but also reduces the energy consumption by avoiding unnecessary horizontal detours. Finally, sensitivity analyses indicate that the volumetric water content of soils exerts the strongest impact on the localization and energy performance, the burial depth introduces a moderate but consistent effect, and the clay fraction causes only minor but measurable degradation. These findings indicate the practicality of the proposed framework for efficient and robust localization in diverse WUSNs.

Wireless underground sensor networks (WUSNs) offer significant social and economic benefits by enabling the monitoring of subterranean entities. However, the communication reliability of WUSNs diminishes in harsh environments where terrestrial network infrastructure is either unavailable or unreliable. To address this challenge, we explore the feasibility of integrating buried massive machine-type communication (mMTC) sensors with non-terrestrial networks (NTNs), including unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low Earth orbit (LEO) satellites, to establish underground-to-NTN connectivity for various large-scale underground monitoring applications. To assess the effectiveness of underground-to-NTN connectivity, we develop a Monte Carlo simulator that incorporates a multi-layer underground attenuation model, the 3GPP empirical path loss model for various NTN platforms, and two LoRaWAN modulation schemes, i.e., LoRa and LoRa-frequency hopping spread spectrum (LR-FHSS). Our results evidence that LoRa SF7 is a strong candidate for short-range UAV communication in rural environments, while LR-FHSS modulation proves to be a promising option for HAP and LEO satellite platforms in massive WUSNs scenarios thanks to its adequate link budget and robustness to the interference. Finally, we demonstrate that the success probability of underground-to-NTN connectivity using LoRa and LR-FHSS is significantly affected by factors such as the monitoring environment, the number of devices, burial depth, and the soil’s volumetric water content.

Radio-frequency wireless energy transfer (WET) is a promising technology to realize wireless-powered underground sensor networks (WPUSNs) and enable sustainable underground monitoring. However, due to the severe attenuation in harsh underground soil and the tight energy budget of the underground sensors, traditional WPUSNs relying on the channel state information (CSI) are highly inefficient, especially in massive WET scenarios. To address this challenge, we comparatively assess the feasibility of several state-of-the-art CSI-free multiantenna WET schemes for WPUSNs, under a given power budget. Moreover, to overcome the extremely low WET efficiency in underground channels, we propose a distributed CSI-free system, where multiple power beacons (PBs) simultaneously charge a large set of underground sensors without any CSI. We consider the position-aware$K$-Means and the position-agnostic equally far-from-center (EFFC) approaches for the optimal deployment of the PBs. Our results evince that the performance of the proposed distributed CSI-free system can approach or even surpass that of a traditional full-CSI WET strategy, especially when adopting an appropriate CSI-free scheme, applying the advisable PBs deployment approach, and equipping the PBs with an appropriate number of antennas. Finally, we discuss the impact of underground parameters, i.e., the burial depth of devices and the volumetric water content of soil, on the system’s performance, and identify potential challenges and research opportunities for practical distributed CSI-free WPUSNs deployment.

Sustainable operation with extended battery lifetime is always desirable for LoRa-based Wireless underground sensor networks (WUSNs) as the maintenance is generally costly. The link quality of LoRa-based WUSNs is strongly correlated with the LoRa physical layer (PHY) parameters and the characteristics of soils (e.g., the soil moisture). Thus, the strategy of optimum transmission of underground node needs to be carefully designed to establish and maintain reliable connectivity with a low power consumption under dynamic environmental conditions. By exploiting the local soil moisture to estimate the underground path loss based on the proposed channel model, an adaptive selection mechanism of the LoRa PHY parameters is implemented for underground nodes. In this paper, we use the real soil moisture data to numerically evaluate the performance of the proposed adaptive selection mechanism. Simulation results successfully verify that, compared with other solutions, the proposed scheme can achieve at least 60% reduction in the annual transmission power consumption while high link quality is maintained. Moreover, the proposed mechanism embodies the advantages of LoRa based WUSNs for ultra-low power and negligible delay.

A variety of industrial applications are deployed in underground environments, such as soil condition assessment and pipeline monitoring (PM). Wireless underground sensor networks (WUSNs) are capable of continuously monitoring pipelines and promptly alerting any anomaly of entities. However, underground soils significantly influence the traditional WUSNs connectivity success. Long range (LoRa), being a leading low-power wide-area networks (LPWANs) technology, provides a new solution for underground industrial monitoring with its advantages in long-range capability and ultralow power consumption. Nevertheless, the LoRa-based link quality characteristics have not yet been quantitatively evaluated for WUSNs. In this article, the channel models of both the underground-to-aboveground (UG2AG) and aboveground-to-underground (AG2UG) communications are investigated. We experimentally analyze the impact of the propagation direction, burial depth and LoRa physical layer parameters on the in-situ LoRa propagation performance. The received signal strength indictor (RSSI), signal-to-noise ratio (SNR), and packet deliver ratio (PDR) are characterized for both communication channels in LoRa-based WUSNs. The semiempirical path-loss models are successfully verified by our field results, and we demonstrate that the communication range can be greater than 50 m at the burial depth of 0.4 m by adjusting the LoRa transmission/receiving settings. The combination of RSSI and SNR can be a better indicator of PDR than relying on either of them alone. Finally, the frame error rate (FER) is calculated to estimate the link performance with EM interferences. These results successfully demonstrate the advantages of LoRa for PM applications, which serve the first step toward the efficient protocol development of LoRa-based WUSNs.

Deeply buried inclusions such as pipes and cables cannot be detected when the air-ground interface suffers severe impedance mismatch, resulting in little electromagnetic (EM) signals penetrating the subsurface, even before the scattering and reflection from the buried inclusions occur. Therefore, increasing the penetration depth by effectively enhancing the EM transmission into the lossy subsurface domain is of great importance. In this article, we present our simulation and experimental results of a type of antireflection metasurfaces that can sufficiently enhance the transmission from the air to the subsurface for ground-penetrating radar (GPR) applications. The proposed metasurface design consists of an array of closed ring resonators (CRRs) and metallic mesh on each side of a dielectric spacer, showing near-perfect antireflection. The corresponding enhanced transmission is only limited by the material losses of the metasurface itself. Through the geometry optimizations, three metasurface designs have been numerically and experimentally demonstrated for the dry, medium moist, and wet scenarios. It is discovered that the transmission into the wet foam brick can be increased by up to 50% when the metasurface is in place. The metasurface-based transmission enhancement is also relatively insensitive to the deviation of the permittivity of the material under test (MUT). Our real-world GPR experiments demonstrate that an undetectable buried pipe can be distinguished if the metasurface is placed at the air-ground interface. The proposed metasurface approach provides a promising solution to the impedance matching problems for nondestructive testing applications.

A reliable communication channel between the underground sensor nodes and aboveground devices is vital and mostly desirable for data collection, relay and upload for the wireless underground sensor networks (WUSNs). In particular, the analysis of the underground-to-aboveground (UG2AG) link quality is crucial for the practical realization and deployment of WUSNs. Recently, LoRa, as one of the low-power wide-area networks (LPWANs) technologies, has attracted substantial attentions for its unique advantages such as the low power consumption and long-range capability. However, to the best of our knowledge, its performance has not yet been quantitatively analyzed for LoRa-based WUSNs. In this paper, a preliminary evaluation of the LoRa's capability of serving a UG2AG channel are presented at various burial depths in two challenging media, i.e., attenuative soils and water. We experimentally analyze the influence of various physical layer (PHY) parameters, e.g., spreading factor (SF), bandwidth (BW), and coding rate (CR) on the LoRa's propagation performance in the field. The results reveal that the reliable UG2AG communication link (without packet loss) can be established at an internode distance greater than 50m when the LoRa device is buried 0.4m deep, even in soils with relatively high volumetric water content (VWC). Our field results successfully verify the theoretical model for the UG2AG channel. These results may have a significant impact on the deployment of LoRa-based WUSNs with the potential applications in underground utilities monitoring.