# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 1 Introduction  
The design of the topology and deployment density of Wireless Sensor Networks (WSNs) are highly affected by the signal path loss estimation for each system in its intended environment. Wireless Underground Sensor Networks (WUSNs), as a subcategory of WSNs, have a variety of applications in precision agriculture, buried infrastructure monitoring (i.e., pipeline monitoring) and mine monitoring [1; 2]. However, the environments that WUSNs operate in are drastically different from those for common terrestrial WSNs. These differences impose design and operational limitations on the WUSNs. The lack of available power and large path loss (caused by the poor radio frequency transmission through soil) impose boundaries on routing and topology of the networks, and are the main challenges for WUSNs [3]. Signal transmission in a WUSN can be divided into three main categories of Underground to Underground (UG2 UG) and Underground to Aboveground (UG2 AG and AG2 UG). From a system viewpoint, Aboveground to Aboveground (AG2 AG) may also be important for ultimately transmitting any information to the end user, but this is not part of the current assessment. Figure 1 shows a schematic of a typical WUSN used for pipeline monitoring. UG2 UG communications in WUSNs can be further categorized, based on their depth, into topsoil communications (0-30 cm depth) and subsoil communications (>30 cm depth). This classification aims to separate the effect of the soil-air boundary on the attenuation of the signals (i.e., reflections). The majority of buried infrastructure (e.g., water pipes) is placed in the subsoil region [4, 5] and therefore the focus of this paper is on underground to underground wireless communication in this region. While the attenuation of Electromagnetic (EM) signals in air is well understood and extensive research exists [6, 7, 8] on the path loss models for terrestrial WSN in various applications, the complex and dynamic nature of the soil have made it difficult to accurately estimate the attenuation of EM signals in soil. Many of the properties of soil, such as water content, composition, and density, greatly affect the attenuation of EM signals by changing the dielectric properties of soil [3]. In addition, these properties are not constant and can vary due to environmental factors (i.e., water infiltration due to rain). The path loss of the signals in the soil where the WUSN nodes are deployed should be accurately estimated in order to design an efficient and reliable WUSN. Moreover, the range of variations in the path loss due to environmental/seasonal changes should also be considered in the design of the WUSNs. An environment with a higher path loss can dictate the spatial density of deployment of the underground nodes to ensure reliable connectivity. Soil can be defined as a complex mixed dielectric medium, which consists of air, water (bound and free) and bulk soil [9]. The composition of the soil and its water content are the two factors that are mainly used in empirical models for the estimation of the dielectric properties of soil [10]. Dielectric properties of soil can also be estimated based on Time Domain Reflectometry (TDR) and Vector Network Analyser (VNA) measurements [11, 12]. The existing research [5, 13, 14, 15] only reports on the performance of one model and/or one location, thereby making a more comprehensive comparison of the models/locations impossible. To the best knowledge of the authors, this paper, for the first time, compares the accuracy of common models for the estimation of RF attenuation with experimental measurements of attenuation in soil that have been undertaken at three separate field trial locations each having different soil compositions and conditions. This allows the models to be directly compared with each other at each location. In addition to the existing model approaches, a new approach for estimating the RF attenuation in soil is also investigated that proposes the use of permittivity values extracted from TDR measurements rather than mixing models [10].