## 4 IoT for Geospatial Research
### IoT Devices and Sensors  
A wide variety of IoT devices has been employed in the surveyed work, including RFID tags [52; 58], barcodes and QR codes [9], and open hardware platforms such as Arduino [40; 66]. Mobile phones and their embedded sensors (i.e., cameras and microphones) have been used in many studies [13; 19; 45; 57; 65], especially in studies dealing with participatory sensing and crowdsourcing [22; 23; 25; 61], and tourism services [20; 51]. The majority of studied papers, especially those where mobile phones were used, also harnessed GPS sensing for acquiring exact user location. Moreover, ultrasonic water sensors [21]; pollution sensors [13]; infrared (IR) cameras [63]; temperature, humidity and pressure sensors [46]; geocubes, meteorological and hydrological sensors [26]; buoy, pressure and water column heights sensors [27]; temperature, dissolved oxygen and pH sensors [40]; and 3D accelerometer and gyroscope sensors and animal collar tags [59; 60] have been recorded. Asset management was facilitated by means of combining RFID tags and Global Navigation Satellite System (GNSS) receivers [58], with GNSS being used also in [66]. Transportations systems make use of vehicular sensors for road safety [54], smart traffic lights and road sensors [55], air quality, traffic conditions, sensors for parking availability [12], and traffic systems in general [56]. For enhanced experiences in museums and cultural heritage areas, smart glasses and wearable devices have been used [57]. Satellites as imaging sensing devices have been harnessed in problems related to biodiversity [47; 49], livestock agriculture and environmental impact [35; 50], forest fire risk assessment [33], vulnerability assessment [34] and disaster relief [61] in earthquakes, disease monitoring [64; 65], for identifying groundwater potential zones [39] and in tsunami evacuation planning [36] (LiDAR imagery). Furthermore, meteorological stations (i.e., air, temperature and precipitation) were used for climatological modeling [70] and for the control of diseases [64], while radar data were also used for recording precipitation in flood forecasting [32]. In studies relevant to analysis of surfaces and geostatistics, groundwater sample sensors were harnessed for detection of heavy metals [69], surface soil sampling [71], and sampling for zinc contamination [68]. Spectrophotometers were also used in [69], while acoustic data, conductivity, temperature, and depth sensors have been recorded in [67]. Not many papers revealed information whether the IoT devices were placed for long-term or only temporarily to get some measurements or perform sampling. Long-term sensor deployment was observed in water monitoring systems [21; 40], in air quality monitoring [46], in monitoring landslide displacements [26] (using low-power 10-Watt solar panels), in deep-ocean tsunami measuring [27] (battery-powered with four-year lifetime), in vehicular sensor networks and transportation systems [12; 42; 54; 55], in weather monitoring stations [70] and in wildlife monitoring [60]. Apparently, long-term deployments were provisioned for complete systems (e.g., transportation) or in environments where sensor replacement was expensive or difficult. An interesting matter from the IoT perspective is sensor discovery, i.e., how sensors are discovered automatically from their peers, in cases of IoT-based wireless sensor networks [74; 75]. Unfortunately, only few papers revealed such information. An online directory platform called Pachube was used in [13] for sensor registration on the web, and CityPulse used a dedicated web platform as sensor directory [12]. A web platform called OnTAPP was also used during the monitoring of dengue fever [48]. Natural language processing and text data mining were used in [29], to discover content (indirectly) through the web. Short- and long-range broadcasting were employed in [60], for wildlife monitoring.