An Attenuation Model of Node Signals in Wireless Underground Sensor Networks

Wireless underground sensor networks (WUSN) consist of sensor nodes that are operated in the soil medium. To evaluate the signal attenuation law of WUSN nodes, in this study, a WUSN node signal transmission test platform was built in the laboratory. The signal intensity data of WUSN nodes under different experimental conditions were obtained by orthogonal test. The WUSN node signal attenuation model was established. The test results show that the transmission of WUSN node signals in the soil medium is seriously affected by soil moisture content, node burial depth, soil compactness, and horizontal distance between nodes. The R2 of the models was between 0.790 and 0.893, and the RMSE of the models was between 2.489 and 4.192 dbm. Then, the WUSN node signal attenuation model involving the four factors was established. The R2 and RMSE of the model were, respectively, 0.822 and 4.87 dbm. The WUSN node signal attenuation model established in this paper can facilitate WUSN node deployment.

Energy-Spectral Efficiency Optimization in Wireless Underground Sensor Networks Using Salp Swarm Algorithm

Achieving high data rate transmission is critically constrained by green communication metrics in Wireless Sensor Networks (WSNs). A unified metric ensuring a successful compromise between the energy efficiency (EE) and the spectral efficiency (SE) is, then, an interesting design criterion in such systems. In this paper, we focus on EE-SE tradeoff optimization in Wireless Underground Sensor Networks (WUSNs) where signals penetrate through a challenging lossy soil medium and nodes’ power supply is critical. Underground sensor nodes gather and send sensory information to underground relay nodes which amplify-and-retransmit received signals to an aboveground sink node. We propose to optimize source and relay powers used for each packet transmission using an efficient recent metaheuristic optimization algorithm called Salp Swarm Algorithm (SSA). Thus, the optimal source and relay transmission powers, which maximize the EE-SE tradeoff under the maximum allowed transmission powers and the initial battery capacity constraints, are obtained. Further, we study the case where the underground medium properties are dynamic and change from a transmission to another. For this situation, we propose to allocate different maximum node powers according to the soil medium conditions. Simulation results prove that our proposed optimization achieves a significant EE-SE tradeoff and prolongs the network’s lifetime compared to the fixed allocation node power scheme. Additional gain is obtained in case of dynamic medium conditions.

Theoretical and Experimental Studies on the Signal Propagation in Soil for Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUSNs), an important part of Internet of things (IoT), have many promising applications in various scenarios. Signal transmission in natural soil undergoes path loss due to absorption, radiation, reflection and scattering. The variability and dynamic of soil conditions and complexity of signal attenuation behavior make the accurate estimation of signal path loss challenging. Two existing propagation models for predicting path loss are reviewed and compared. Friis model does not consider the reflection loss and is only applicable in the far field region. The Fresnel model, only applicable in the near field region, has not considered the radiating loss and wavelength change loss. A new two stage model is proposed based on the field characteristics of antenna and considers four sources of path loss. The two stage model has a different coefficient m in the near field and far field regions. The far field distance of small size antenna is determined by three criteria: 2 D2/λ, 5 D, 1.6 λ in the proposed model. The proposed two stage model has a better agreement with the field experiment data compared to Friis and Fresnel models. The coefficient m is dependent on the soil types for the proposed model in near field region. It is observed from experiment data that the m value is in the range of 0~0.20 for sandy soils and 0.433~0.837 for clayey silt.