# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 3 Remote Sensor Network Architecture  
As stated before, the use case of this article is an IoT telemetry service to monitor remote WSNs in Antarctica interconnected through NVIS LFNs. The monitored data are used for permafrost studies and are gathered by GTN-P stations [9], which are the sensors of our network. Each of these GTN-P stations senses 32 different values hourly, and these values must reach the remote control center in Europe. The GTN-P stations are equipped with a Moteino [64], an Arduino-based board designed for low-power consumption applications. The Moteino will send, through LoRa, its sensed values to a Raspberry Pi 3B+ gateway acting as a concentrator (access network). LoRa was preferred over other alternatives (e.g., Sigfox, NB-IoT) as the access network protocol because of its teleoperator independence. The LoRa network will be configured with a transmission frequency of 868 MHz, a code rate CR3 (4/7), and a spreading factor SF7, obtaining a 125 kHz channel bandwidth with a bit rate of 5.47 kbps. As proved in [65], this configuration can offer a coverage range of up to 30 km in Antarctica. Figure 2(a) shows the Moteino board with the LoRa transceiver that will be used during the campaign to collect and forward the data from the GTN-P stations. The Raspberry Pi 3B+ gateway will forward these data through NVIS links (backbone network) to the Internet edge router in the Uruguayan Artigas Base in Antarctica. NVIS was preferred over satellite communication because the latter presents coverage issues in polar zones and has a higher economic cost [3]. The NVIS nodes will be configured to transmit at the 4.3 MHz transmission band, with a channel bandwidth of 2.3 kHz and a bit rate of 4.6 kbps. As in [3], we will increase the NVIS transmission reliability with an FEC convolutional code (1/2 rate code) and interleaving. With this configuration, an NVIS link range is up to 250 km. Figure 2(b) shows the NVIS node with the Raspberry Pi 3B+ gateway, and Figure 2(c) shows the NVIS antenna (inverted vee antenna). From the closest NVIS node to the Internet edge router (the one with Internet connectivity), data will be pushed to the Internet. From this moment, data monitoring and gathering will be available remotely from the control center. Figure 4 shows the network architecture diagram of the remote WSN. The Artigas Base's Internet connectivity is supposed to have high reliability, so our trustworthiness assessment is focused on the access network (LoRa) and the backbone network (NVIS). As mentioned before, the reliability of NVIS links is very dependent on the ionosphere state, so it is not possible to send data during the night as all of it would be lost. For this reason, we believed it was necessary to apply a DTN technique to prevent the loss of data gathered during the night. In our case, we apply the DTN in the backbone network, as it is more likely to suffer from a lack of end-to-end connectivity, long delays, and network disruption. Given that, in our case, we can predict a specific time slot when the NVIS links do not work (nighttime), we opted to implement a lightweight DTN approach, opportunistically sending the data collected during the whole night as a bulk transfer when the NVIS channel becomes available in the morning. Each concentrator should have collected 13 different sets of sensed values from each GTN-P station during the night. Our project requires that, on average, at least 9 out of the 13 datasets gathered from each station (around 70%) reach the control center correctly [7]. The DTN is usually implemented as an overlay network below the application layer of the Open Systems Interconnection model (OSI model) and needs a convergence layer as an interface to connect to the lower layers of the protocol stack. Figure 5 shows the protocol stack from our use case. Figure 3: Antarctic WSN Hardware. (**a**) Moteino node with LoRa transceiver. (**b**) NVIS node with Raspberry Pi 3B+ gateway. (**c**) NVIS inverted vee antenna.