## 4 IoT for Geospatial Research
### Reliability  
The reliability of IoT devices and the accuracy of their measurements is an important topic, however most authors did not provide any relevant details. Reliability is essential in healthcare and health monitoring applications [9]. The need for trust in participatory sensing is underlined in [22]. In crowdsourcing, sometimes the data are incomplete, such as at the noise map application [25], where the source of noise (e.g., train and airplane) was missing. Lack of complete and accurate crowdsourced information complicated rescue and recovery efforts during the Haitian earthquake [61]. Absence of high-quality detailed surveillance data was existent in most malaria-endemic countries [65].  
Lack of accuracy was a dominant issue in interpolation/kriging scenarios, where missing data created certain percentages of errors when averaging/interpolating [56; 71]. Uncertainty in soil heavy metal pollution assessment was observed in [71]. The authors in [56] claimed that high-quality data assessment is required. This need to meet data quality objectives (DQOs) was stressed in [46], during the design of an air quality platform. Sensors needed to be evaluated for indicative measurements, examining a list of parameters that affect the electrochemical sensor responses.  
Dealing with missing data was an issue also in [32], towards precise flood forecasting for the River Meuse. Further, inaccurate data occurred in [43], when analyzing taxi rides. Not all entries were accurate, while _some rides went to impossibly far away places, took way too long, or were charged with immensely high fares_. The analysis of crime potential in [41] focused upon one unique measure of violence, making the assumption that victims appear in hospital after a violent assault, while this might have not been exclusively the reason in real life. Absence of detailed data makes this assumption necessary.  
Calibration of the sensors is important in many cases, and it was a necessary procedure in the pollution sensors used in [13]. The authors needed to send the sensors back to the manufacturer for accurate calibration. The problem with faulty readings is that it is sometimes hard to detect, if ground-truth information is not available to compare with. Another general problem with calibration is that _extreme values are more difficult to predict than mean values_[70]. Elaborate calibration of the high-quality tsunami measuring equipment took place in [36], while instrument calibration of the Arduino-based sensor modules was done also in [40].  
Some reliability problems occurred during installation of the sensors at the field. The geocubes used to monitor landslide displacements in [26] experienced loss of observations due to a lack of sealing and improper solar panels used as energy sources. The sensors used to assess irrigation water well suitability were forced to become immediately sealed after sensing [69], because exposure to air affected the measurements. In addition, the GNSS receivers used in [58] experienced low accuracy and weak GNSS signals in high building-density areas, and suggested a _tag location principle_ technique to improve this accuracy. Sealing and insulation of the sensor devices was performed in [66] to protect them from the marine environment, the hot summer temperatures and the presence of corrosive elements such as sand and seawater.  
Finally, accuracy issues may appear also in remote sensing, due to the resolution of satellite-based imagery used. The satellite sensor used in [64] recorded a surface temperature averaged over an 8 km \(\times\) 8 km area, with an estimated RMSE error of 2.3-4.0 \({}^{\circ}\)C.