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**[PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Radio Link Model for Node Deployment in Underground Mine Sensor Networks. **[PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Radio Link Model for Node Deployment in Underground Mine Sensor Networks. **[PERSON]. [PERSON], [PERSON]; [PERSON] reflection from the walls and diffraction produced by lossy minerals [4; 5; 6]. In [7], a theoretical model was proposed to describe radio wave propagation in straight rectangular tunnels. Additionally, ref. [8] reported on the typical structures of underground mines and road tunnels. The multi-mode model proposed in [8] suggests that node positioning should be kept in the middle of the tunnel cross-section in order to achieve the best possible performance. It is also reported that the behavior of radio waves in the presence of lossy walls needs to be modeled more precisely and analytically so that optimal node position and separation between the nodes can be predicted accurately [9; 10]. It is necessary to understand the mine environment and to derive a radio propagation link model for node deployment in an underground mine sensor network. This will help the radio link designers to estimate the underground communication range and efficient positioning of sensor nodes. In a mine environment, radio waves can be absorbed or reflected by lossy mine walls, metal objects, and the gaseous environment, leading to attenuation and multi-path fading [9]. For example, the lossy mine walls can cause significant attenuation of the radio signal, particularly at higher frequencies. Metal objects, such as mining equipment and vehicles, can also cause reflection and scattering of the radio waves, leading to interference and signal distortion. The gaseous environment in mines can also affect radio propagation. The presence of gases, such as methane and carbon monoxide, can absorb or scatter radio waves, leading to attenuation and fading. The humidity and temperature of the mine environment can also affect the dielectric properties of the medium and alter the propagation of radio waves. To account for these factors, the radio propagation model needs to be re-evaluated using appropriate techniques and tools, such as ray tracing, electromagnetic simulations, or measurements. To the best of the authors' knowledge, the available radio propagation models could not determine the node position and node separation for underground mines when considering real-time measured data and experimental characterization. Owing to a higher path loss at GHz frequencies, the range of Zigbee and Bluetooth transceivers is quite limited, which is why the proposed radio model experiments were carried out on off-the-shelf wizzi sensor motes operating at 433 MHz with DASH7 [3; 11]. Recently, 433 MHz technologies have recaptured the attention of researchers as a substitute for 868/915 MHz and 2.4 GHz bands. Currently, there are two physical layer standards available at 433 MHz, namely, DASH7 Mode 2 [11] and IEEE 802.15.4f [12]. Besides that, the 433 MHz band has relatively good propagation features due to its lower frequency; it is less crowded compared to the 2.4 GHz band and bears fewer external interferences [2]. Prior to this study, to the best of our knowledge, there has been no characterization of salt mines at 433 MHz in complex structural scenarios. In the literature, high-gain electrical dipole antennas have been used, which increase the separation range but are only effective for the middle node position [13; 14]. Quarter-wavelength monopole antennas with omnidirectional radiation patterns and 3 dBi gain were used for the experiments in this study. Table 1 lists the key parameters of similar wireless technologies [15; 16; 17], such as operating frequency, range, data rate, power consumption, and other features of each technology side-by-side for ease of reference in subsequent sections.