# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### IoT Nodes  
An integral component of an IoT device is a power supply. To supply the device with power, many options are available, the simplest being a connection to a power grid; an example is an AC power monitoring module with an integrated wireless connection via WiFi [61]. Power grids provide sources of stable and reliable energy, but they are mainly only available in urban locations. Another option is the use of a battery [62]. Batteries also provide stable sources of energy, but they require periodic charging and replacement. Battery-powered IoT devices can also be designed to operate for long life spans [10]. Another option for supplying electricity is through energy harvesting methods [63]. Characterized by many advantages, energy harvesting modules are small, maintenance-free and inexpensive solutions, especially compared to battery-powered options. The main drawback of energy harvesting solutions, however, is limited available energy.  
Table 5 summarizes IoT power supply technologies according to the energy transducer type. Selecting a suitable power supply type is strongly dependent on the target application [64]. In many geothermal applications [65], power grids are readily available, and generally, no energy harvesting or battery technologies are required. However, IoT measurement devices are often deployed directly in geothermal boreholes, where the installation of a wired power supply is complicated [66]. Energy harvesting is therefore a highly suitable technology for these applications.  
Figure 5: IoT architecture and application domains.  
Figure 6: Block diagram of a typical IoT solution.  
Energy harvesting using solar panels or small wind turbines is suitable only for surface deployment, where sunlight and wind are sufficient [72]. For underground structures, thermoelectric generators (TEGs) have significant potential as an energy harvesting technology. TEGs exploit the Seebeck effect to generate energy through temperature differentials [73]. The main challenge with this technology is finding suitable temperature differences or gradients directly in a borehole for exposure to the TEG.  
Acting as brains, microcontrollers (MCUs) regulate IoT node behavior, such as sensor data collection, local memory storage and data transmission to the cloud. Since IoT nodes generally have only limited energy for operation [74], MCUs are also responsible for energy management. Naturally, they are selected from ultra-low-power series offered by silicon manufacturers to minimize the MCU's own consumption. Examples of these series types are STM32L [75], NXP K32L [76] and Microchip PIC32 MZ [77]. Specific models are selected according to the application, and generally, higher performance requires more energy. ULP (ultra-low-power) MCUs typically offer multiple switchable modes of operation and the ability to switch off individual peripherals (clock gating) for effective power management. Switching off all components not currently in use thus reduces the overall energy consumption [78; 79].  
IoT nodes are usually equipped with local data storage, which can be used for either the long-term storage of accumulated values or short-term storage (as a memory buffer). As with other IoT node components, power consumption is one of the crucial parameters in selecting memory and is dependent on the type of memory used. Ferroelectric Random-Access Memory (FRAM) is a type of memory technology with low energy consumption, but its data capacity is limited to a few dozen MB. At the other end of the spectrum of data capacity is flash memory, commonly found in SD cards, with maximum capacities of hundreds of GB. The disadvantage of flash memory is higher energy consumption and significantly slower data write speeds. Data storage memory may form either a discrete component in an IoT node or be integrated into the MCU [80].