# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 2 Related Work  
Although several works were carried out to remotely monitor the structural health, based on IoT technology for different areas, i.e., agriculture [4], civil infrastructure [5], and smart cities [6], few works have explored IoT systems for monitoring the structural health of guyed towers. Most of these works combined monitoring the tower's structural inclination, due to environmental parameters that can accelerate the tower deterioration, such as temperature, humidity, and wind action. Since power grids will inevitably reach remote areas, wireless ad hoc network solutions seem straightforward to achieve remote monitoring. It is possible to learn from the current literature that research works usually focus on one out of two main approaches: network and sensor technologies. Furthermore, some can also present initiatives toward machine learning techniques, which is a current trend that shows impressive contributions in a broad range of applications. Concerning network technology, one of the first attempts for remote monitoring power lines used GPRS mobile to connect to the internet [7]. The system sends sensor data to a monitor for analysis and process. The authors in [8; 9] proposed a wireless tower body monitoring system composed of accelerometers and some environmental sensors. The remote real-time monitoring of towers' health is performed through the combination of ZigBee and GSM. The works cited above focus on measuring the structural health of towers without prioritizing the transmission and data collection strategies. Currently, most research works focus on modern approaches based on the Internet of Things and Low Power Wide Area Networks. For instance, the authors in [10] propose a tilt monitoring system considering sensing device energy consumption, link distance, and cost by adopting Low-Power Wide-Area Network (LPWAN) technology. In addition, the authors propose a LoRa multinode network connected to a real-time online monitoring system through NB-IoT. Researchers on [11] propose an online monitoring system for the tilt of power line towers, using two inclinometer sensors and Narrow Band Internet of Things (NB-IoT) network technology. In [10], the authors adopt adopts LoRa (Long Range) as a Low-Power Wide-Area Network (LPWAN) technology, as well as NB-IoT technology for a mobile data connection as a gateway to the internet. The proposed system forwards online real-time tilt information of the tower to a cloud platform through NB-IoT and LoRa multinode networks. The data history allows administrators to proceed with maintenance according to the faults pointed out by the analysis of tilt information. Notwithstanding network technology, it is essential to present research works focusing on satellite communications. For instance, the authors in [12] propose the use of a traditional Synthetic Aperture Radar (SAR) method to obtain the tower tilt. The main challenge resides in solving the problem of layover areas distortion [13] that usually happens for line-shape objects located vertically over the ground. Thus, the authors propose removing the top-down vertical height phase by obtaining the differential phase caused by inclination displacement. In [14], based on [12], the author proposes a revised algorithm that can remove the top-bottom vertical-height interferometry phase of the tower by using the information of every tower. Then, the residual differential interferometry phase obtained after the phase unwrapping allows calculating the inclination displacement of the top position of the tower more precisely. Finally, in [15], the authors retrieve the inclination direction of the power transmission tower using geocoding. First, the backscattering coefficient separates the power line tower from the background, and a refining processing marks the tower's position in the image. Finally, a distance-Doppler model retrieves the geographical coordinates of those marked points. The authors claim that the calculated tilt results have a 0.0025\({}^{\circ}\) error.