## 1 Introduction  
A wireless sensor network (WSN) is composed of sensor nodes that are able to communicate with each other through wireless channels [1; 2]. With the growth of research on WSNs in both academia and industry, WSNs have been used in a wide variety of applications, such as infrastructure monitoring, industrial control, environmental sensing, and surveillance. An increasing number of practical applications prompts WSN research to be more realistic in every aspect. As with any other wireless communication system, the propagation model is an essential and critical tool for WSN planning and deployment [3]. The propagation model describes the radio channel characteristics, which are closely linked with the environment and physical parameters. By predicting the attenuation and distortion of the radio-frequency signal, the expected received signal strength level can be calculated. Many studies reveal that there is a complex relationship among the propagation characteristic, the sensor placement, and the network connectivity [4]. Most existing research on WSNs implies the basic assumption of accurate propagation models. However, wireless channels can be easily affected by interference, noise and other factors, and the fading characteristics are closely related to the communication environment. The WSN would not perform as expected under realistic scenarios if simplistic or idealized propagation models are adopted. Since WSNs are often deployed in specific areas to monitor various environmental indicators in real-time [5], especially in harsh areas for emergency applications, an appropriate propagation model based on specific environment and terrain is of the utmost importance to adequately predict the link quality and coverage of WSNs. Most previous studies of radio propagation models assume that antennas are placed at least 0.5 m above the ground [6]. However, in most WSN applications, sensors are thrown directly on the ground. Hence, existing propagation models cannot correctly reflect the real characteristics of these truly near-ground wireless channels. In one of the early studies of near-ground propagation characteristics [7], the authors investigated short-range near-ground scenarios using a two-ray model, and noticed a significant decrease in signal strength as transceivers get closer to the ground. [PERSON] et al. [8] showed results from narrowband and wideband measurements conducted at 300 MHz and 1900 MHz investigating effects of transmitting and receiving antenna heights, antenna radiation patterns, and effects of foliage in a forest environment, but the antenna height was set to 0.75-1.55 m, which is not close enough to ground. In [9], the near-ground propagation channel measurements at 868 MHz in three different scenarios are implemented. In [10], outdoor measurement and modeling of three near-ground sites are conducted at 2.4 GHz frequency, and the prediction accuracy of one- and two-slope models are compared. In [11], the authors obtained the path loss models of outdoor and indoor scenarios at 3.5 GHz, using simple one-slope model, with antenna heights of 0.3 m and 2.1 m, respectively. The effect of vegetation barriers is also taken into consideration. [PERSON] and [PERSON] [12] listed some constraints when exploring WSN specific propagation models, such as low antenna heights, directivity of antennas, low transmission power and network topology, where the authors briefly introduced five optional propagation models for near-ground WSNs. [PERSON] et al. [13] measured characteristics of the outdoor near-ground propagation, and found that the path loss shows crucial dependence on antenna heights. The authors indicated that the path loss has slight dependence on frequency. Other studies (e.g., [14; 15; 16]) propose empirical path loss models for different outdoor environments, and confirm that the type of terrain can have a significant impact on radio propagation near ground.