## 1 Introduction
This is particularly true in some segments of the SG such as substations or solar plants/wind farms. There, the different elements are monitored (via IoT devices) and their information aggregated (once sent to a gateway) before being transferred to a central processing station (through wired internet networks), where data are stored and analyzed. Therefore, the work cycle of IoT devices operating in such environments, generally boils down to sensing some physical parameter (s) (temperature, pressure, humidity, etc. ), interact with other devices periodically or on demand, and finally, provide some form of added-value service (such as a complete report on the status of a specific transformer or the energy generation rate of some windmills). This gives rise to a very relaxed duty cycle; allowing devices to remain silent and dormant most of the time in order to reduce their power consumption. Therefore, in some IoT networks deployed in SG, there is no pressing need to employ \"high\" data rates, and it seems logical to explore the advantages and disadvantages of using other unlicensed standardized ISM bands with smaller bandwidth. Off-the-shelf IoT devices that make use of the 802.15.4/6 LoWPAN/ZigBee low-power standards operate in one of the three following bands: 2400, 915 or 868 MHz [19]. While the two former bands have more than ten channels in which IoT devices may work; the 868 MHz band only offers one channel. This issue may represent an unsurmountable obstacle in scenarios where the large number of IoT devices may force the use of channel-assigning algorithms to prevent an increase of interferences and collisions [20; 21; 22]. Therefore, as a smaller-bandwidth alternative to the 2400 MHz band, the 915 MHz one may represent an option for IoT-based SGs worth studying. As a matter of fact, although such a band works at slower rates (40 Kb/s), it has some appreciable benefits; among them, we should note two that are crucial in the SG arena: longer coverage and less power consumption. In this context, the main contribution of this paper is an exhaustive and quantitative analysis of the impact of the working frequency band (915 MHz or 2400 MHz) on an SG-oriented IoT network. We have focused our study on an SG's generic-enough substation for two reasons: firstly, it is a representative scenario in which the SG may benefit from smaller transmission bandwidths. And secondly, very few works have studied this environment in depth from the wireless communication perspective [23]. Given the complex nature of such a scenario (full of large metallic structures and high levels of electromagnetic noise), we have characterized it for the two aforementioned bands. Hence, the second contribution of this work: a dual-band propagation model focused on IoTs operating in SG's substations. This second contribution is critical as reckoning with an accurate propagation model is of paramount importance in evaluating different IoT environments with high precision. To evaluate both bands, we have incorporated this propagation model in TOSSIM [24], a very well-known and extensively employed simulator for IoT/WSN networks. We have also extended TOSSIM with the ability to simulate the 915 MHz band and the ability to support different line-of-sight visibility conditions for IoT devices deployed in the same network. This way, and with the help of several information-extraction scripts programmed for this work, we have analyzed and derived valuable information from different simulated environments. Both, the propagation model and the extensions/tools for the TOSSIM simulator have been made publicly available in [25]. It is worth mentioning that although this paper is focused on analyzing and quantifying the performance of the 915 MHz and 2400 MHz bands, the results derived for the 915 MHz band are translatable to the 868 MHz one. This is due to the small frequency gap between these two bands (60 MHz in the worst case) and the relatively large coherence bandwidth (CBW) of the channel (in average, 94.20 MHz).