Wireless Underground Sensor Networks Research Articles

Channel modeling for MI-based wireless underground sensor networks with conductive objects

Channel modeling for MI-based wireless underground sensor networks with conductive objects

Wireless Underground Sensor Communication Using Acoustic Technology.

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin-Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs.

An energy efficient routing algorithm in wireless underground sensor networks

An energy efficient routing algorithm in wireless underground sensor networks

Conductor-aware Wireless Underground Sensor Networks Based on Magnetic Induction: Research Challenges

Conductor-aware Wireless Underground Sensor Networks Based on Magnetic Induction: Research Challenges

Wireless Underground Sensor Networks: A Comprehensive Survey and Tutorial

The Internet of Things has developed greatly over the past decade to cater to many diverse applications across almost all fields of life. Many of these applications can either profit or even explicitly require deployment underground, such as precision agriculture, but also land, pipeline, or mine monitoring. Underground deployment offers many advantages, such as concealment of the devices for their protection. However, the underground environment is also very challenging, especially for wireless communications and energy harvesting. In this survey and tutorial, we offer a comprehensive view of the complete topic, from theoretical foundations of wireless communications underground, through system architectures and applications, to energy harvesting options. These topics cannot be viewed separately from each other, as they are deeply intertwined and all of them need to be considered before a possible deployment. We will show that wireless underground sensor networks have a great potential for a variety of applications and are an intriguing alternative to overground deployments. We will describe the state of the art in a tutorial style, so that beginners can also profit. Last but not least, we will identify remaining challenges to guide researchers in this area.

Retracted: Simulation Test of Wireless Underground Sensor Network in Stadiums

Retracted: Simulation Test of Wireless Underground Sensor Network in Stadiums

FuzDeMa: A portable fuzzy-based decision-making tool for reliable communication in wireless underground sensor networks

The deployment and the exploitation of a Wireless Underground Sensor Network (WUSN) remain challenging because of signal attenuation in the soil and the limited battery that powers the sensor nodes. Due to the attenuation of the signal in the ground, the reception or loss of the sent data depends on the ground conditions, which can change dynamically. However, in existing WUSNs, each node sends the data collected in each round regardless of the signal attenuation. It is well demonstrated that sensor nodes consume the most energy during transmission. Obviously, transmission without receiving any data significantly reduces the lifetime of a sensor node useless. This paper presents a novel fuzzy-based decision-making solution called FuzDeMa that reduces energy consumption by anticipating data losses before transmission. To do so, FuzDeMa assesses in real time the loss or the reception of a packet according to the in-situ node's environments before its transmission and decides whether to send or not the packet based on the computed reliability. To validate the proposed approach, we embed it into a dedicated underground node called the MoleNet and realized real experimentations firstly with an existing dataset and secondly, with precision-measuring equipment to estimate the energy consumption. The results revealed the possibility of prolonging the lifetime of the sensor node by saving up to 81.7876 microjoules in a single round. Additionally, FuzDeMa shows the ability to save energy for up to 46 of additional revolutions, thus extending the life of the sensor node to 32.85% for 140 real transmission cycles. An analytical generalization of FuzDeMa is provided regardless of a specific dataset or sensor node. Thus, we provided the conditions for a random dataset to save the energy with any sensor node that implements FuzDeMa during transmissions.

Refined numerical simulation of signal propagation in soils for wireless underground sensor networks

Wireless underground sensor networks (WUSN) have promising applications in the field of underground structural health monitoring. Signal path loss prediction is a great challenge in the design and onsite installation of WUSN. Existing wireless signal propagation models cannot well predict the total path loss due to the neglection of node detailed structure. This paper develops a numerical model in FEKO to simulate the signal propagation in soils considering the detailed structure of buried sensor node including linear shaped antenna, coaxial feeder, batteries and protecting PVC box. The developed numerical model is validated using the Friis equation in free space and results of outdoor signal propagation experiments. Compared to analytical propagation models, the developed numerical model can well consider the path loss induced by sensor node itself. The proposed numerical model is able to simulate the signal propagation in heterogeneous soils. Signal path loss fluctuates in heterogeneous soils due to the coupling of direct, reflected and refracted waves. Parametric studies are conducted to investigate the effects of soil properties, signal frequency, node orientation and soil heterogeneity on total path loss. An optimal frequency foptimal exists to minimize the signal path loss and foptimal is in the range of 0.56–0.65 GHz for the node in this paper.

Hybrid control for malware propagation in rechargeable WUSN and WASN: From knowledge-driven to data-driven

Rechargeable wireless underground sensor network (WUSN) and wireless aboveground sensor network (WASN) provide information transfer services for acquiring aboveground and underground interactive data. However, due to the limited energy, computation, storage and the open environment, sensors are often attacked by malware and their power supply is usually exhausted earlier. In this paper, a novel model with two-layer structure is proposed to depict the dynamic process of malware cross-propagation between rechargeable WUSN and WASN, which takes into account both infection delay and low energy. To further overcome the problem of malware propagation and battery depletion, three hybrid control strategies including continuous recovery, impulse quarantine with isolation delay and pulse charging are proposed. Then, to suppress malware propagation under the minimum cost, the necessary conditions for the model-based knowledge-driven optimal control (MKOC) are solved by the variational method. Moreover, to improve the practical utilization of MKOC, the novel neural network-based data-driven control (NNDC) is proposed based on feedforward neural network (FNN), gated recurrent unit (GRU) and long short-term memory (LSTM). Finally, through numerical simulations, the effects of various quarantine parameters on the total costs and the influence of each control strategy on state curves and control curves are revealed, respectively; meanwhile, the effectiveness of MKOC and NNDC is evaluated by comparing their control costs.

A feasibility study of LoRaWAN-based wireless underground sensor networks for underground monitoring

With the continuous expansion of human interaction with the underground space, the observation of underground environment (e.g. soil moisture) and underground infrastructure (e.g. underground pipelines), as well as the monitoring and prevention of geological disasters (e.g., landslide and urban sinkholes) has attracted increasing attention and become a research focus. However, most of the traditional observation technologies are still facing difficulties in dynamic and continuous in-situ observations/monitoring within the complex underground environment. To solve this problem, this study adopts LoRaWAN-based wireless underground sensor networks (WUSNS) to monitor the underground environment or infrastructure. Furthermore, the feasibility of LoRaWAN-based WUSNs for underground monitoring is systematically demonstrated and presented in this article. Based on our recent validation of LoRa-based underground point-to-point channel model, we evaluate its network-level performance in this paper. We firstly develop a network simulator of LoRaWAN-based WUSNs to provide the feasibility analysis and experimental guidance for underground monitoring. Using this simulator, we numerically demonstrate that the LoRaWAN-based WUSNs technology is capable of underground monitoring over a large area and at great depths. We further certify that an appropriate selection of the physical layer parameters in various underground conditions is beneficial for underground observation. Finally, the balance between data receiver rate and network energy consumption can be implemented using different physical layer parameters for various underground monitoring applications. These results successfully demonstrate the efficient protocol development of LoRaWAN-based WUSNs and their applicability for underground monitoring applications.

Subterranean mMTC in Remote Areas: Underground-to-Satellite Connectivity Approach

Wireless underground sensor networks (WUSNs) promise to deliver substantial social and economic benefits across different verticals. However, many of the relevant application scenarios are located in remote areas with no supporting infrastructure available. To address this challenge, we conceptualize in this study the underground direct-to-satellite (U-DtS) connectivity approach, implying the reception of the signals sent by the underground massive machine-type communication (mMTC) sensors by the gateways operating on the low Earth orbit satellites. We start by discussing the two alternative architectures for enabling such networks and underline their features and limitations. Then, we employ simulations to model a smart-farming application scenario for studying the feasibility and performance of UDtS mMTC based on LoRaWAN technology, with the focus on the effect of the parameters including the burial depths, soils characteristics, and communication settings. Our results reveal that the LoRa modulation fails to compete with the underground propagation losses and the high packets collision rate. However, the long range-frequency hopping spread spectrum (LR-FHSS) modulation is perspective for U-DtS networks and may enable connectivity for the subsurface nodes located at a depth of several dozens centimeters. Finally, we pinpoint the potential challenges and the research directions for future U-DtS studies.

Enhanced meta-heuristic optimization of resource efficiency in multi-relay underground wireless sensor networks.

Achieving a balanced energy and spectral resource utilization is an interesting key design to extend the lifetime of underground wireless sensor networks (UWSNs) where sensor nodes are equipped with small limited energy batteries and communicate through a challenging soil environment. In this article, we apply an improved meta-heuristic algorithm, based on the Salp Swarm Algorithm (SSA), for multi-relay UWSNs where cooperative relay nodes amplify and forward sensed data, received from the buried source nodes, to the aboveground base station. Hence, the optimal nodes transmission powers, maximizing the network resource efficiency, are obtained and used to select beneficial relay nodes. The algorithm enhances the standard SSA by considering the chaotic map for salps population initialization and the uniform crossover technique for salps positions updates. Simulation results show that the proposed algorithm significantly outperforms the SSA in resource efficiency optimization and network lifetime extension. The obtained gain increases when the number of cooperative relay nodes increases. Furthermore, simulations prove the efficiency of the proposed algorithm against other meta-heuristic algorithms.

Internet of Underground Things in Agriculture 4.0: Challenges, Applications and Perspectives.

Internet of underground things (IoUTs) and wireless underground sensor networks (WUSNs) are new technologies particularly relevant in agriculture to measure and transmit environmental data, enabling us to optimize both crop growth and water resource management. The sensor nodes can be buried anywhere, including in the passage of vehicles, without interfering with aboveground farming activities. However, to obtain fully operational systems, several scientific and technological challenges remain to be addressed. The objective of this paper is to identify these challenges and provide an overview of the latest advances in IoUTs and WUSNs. The challenges related to the development of buried sensor nodes are first presented. The recent approaches proposed in the literature to autonomously and optimally collect the data of several buried sensor nodes, ranging from the use of ground relays, mobile robots and unmanned aerial vehicles, are next described. Finally, potential agricultural applications and future research directions are identified and discussed.

Optimizing energy efficiency of LoRaWAN-based wireless underground sensor networks: A multi-agent reinforcement learning approach

Extended battery lifetime is always desirable for wireless underground sensor networks (WUSNs). The recent integration of LoRaWAN-grade massive machine-type communications (MTC) technology and WUSNs, namely LoRaWAN-based WUSNs, provides a promisingly energy-efficient solution for underground monitoring. However, due to the limited battery energy, massive data collisions and dynamic underground environment, the energy efficiency of LoRaWAN-based WUSNs still possesses a significant challenge. To propose a liable solution, we advocate reinforcement learning (RL) for managing the transmission configuration of underground sensors. In this paper, we firstly develop the multi-agent RL (MARL) algorithm to improve the network energy efficiency, which considers the link quality, energy consumption and packet collisions between packets. Secondly, a reward mechanism is proposed, which is used to define independent state and action for every node to improve the adaptability of the proposed algorithm to dynamic underground environment. Furthermore, through the simulations in different underground environment and at different network scales, our results highlight that the proposed MARL algorithm can quickly optimize the network energy efficiency and far exceed the traditional adaptive data rate (ADR) mechanism. Finally, our proposed algorithm is successfully demonstrated to be able to efficiently adapt to the dynamically changing underground environment. This work provides insights into the energy efficiency optimization, and will lay the foundation for future realistic deployments of LoRaWAN-based WUSNs.

The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam.

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.

A Generalized Mixed Path Loss (MPL) Model for Wireless Underground Sensor Networks

A Generalized Mixed Path Loss (MPL) Model for Wireless Underground Sensor Networks

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

Application of Dielectric-Artificial Magnetic Conductor Composite Substrate to Design Miniaturized P-Band Microstrip Antenna for Wireless Underground Sensor Network

In the modern era of wireless Internet of Things designing a planar antenna at relatively low frequency (P-band) with positive gain and high efficiency is exacting task. In this letter, a planar P-band microstrip antenna (a simple rectangular patch of size 0.16λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ×0.20λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ×0.01λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , smaller than classical quarter-wave patch) for wireless underground sensor network (WUSN) is proposed. The proposed structure yields high gain with the judicious use of four shorting vias, which are collocated along the side edges of the patch with a composite substrate (dielectric-artificial magnetic conductor). The results shows that the designed antenna resonates at 413 MHz with gain and efficiency of 3.1 dBi and 75%, respectively, with a 10 dB impedance bandwidth of 2% (408–416 MHz)

Coverage hole identification & healing in Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUSN) are gaining popularity due to their usage in vast prospective domains. Despite wide potential applications, many research issues in WUSNs need to be dealt with. Coverage Holes in WUSNs are one of the major issues that need attention. Nature-inspired optimization algorithms play a crucial role in modern computing, and for more than three decades, the field has made tremendous progress. Traditional algorithms are being replaced with these and have proved their competitiveness in various domains. In this paper, we have proposed an intelligent optimization algorithm based on the TSA (Tree Seed Algorithm) for patching the holes in the network and deciding on effective patching positions such that it reduces the maximum number of holes in the network. The proposed algorithm makes use of random, best, and spiral tree generation for getting the best optimal locations of sensor nodes to be deployed for healing the target region. First, an energy-efficient deterministic algorithm is applied for hole identification in the network. Then, an optimization-based nature-inspired algorithm is applied to heal the holes. The proposed algorithms and others are simulated through MATLAB and results are computed for the randomly deployed network. The proposed hole healing algorithm MTSA (modified TSA) has been compared with PSO (Particle Swarm Optimization) and TSA algorithms. Simulation results prove the effectiveness and robustness of the proposed algorithm. The proposed hole healing algorithm is suitable for small to large-size networks and reduces the larger number of holes than PSO and the original TSA algorithm.

A Q-Learning-based distributed routing protocol for frequency-switchable magnetic induction-based wireless underground sensor networks

Magnetic Induction (MI) based Wireless underground sensor networks (WUSNs) consist of magnetic-antenna sensors that are buried in and communicate through soil, considered as part of full-coverage 6G wireless system. The wireless channel in MI-WUSNs appears a considerable frequency-selective property due to those conductivity objects in underground environment. This results in that a single frequency is almost impossible to ensure that all sensors in MI-WUSNs are both connected. To this end, the frequency-switch strategy is introduced into MI-WUSNs to cope with its frequency-selective property and ensure network connectivity, forming frequency-switchable MI-WUSNs. In this paper, the distributed routing protocol for frequency-switchable MI-WUSNs is studied to contribute to the network design of underground wireless systems. To be specific, we first map the frequency-switchable MI-WUSN to a multi-layer network and give a description about its routing decision in the Q-Learning framework. Then, we discuss several special designs in system implementation for the proposed routing protocol. In addition, simulation experiments are also considered to verify the convergence and effectiveness of our routing protocol.