# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Cost-Effective Dimension
#### 3.3.1 Overall Cost  
Considering the large size of the farm, it is desirable that the hardware expenses associated with IoT implementation, particularly those for solar panels and sensors, be regulated at a reasonable level in order to encourage farmers to adopt the system. Therefore, cost must be considered while designing an irrigation system.  
Only using soil moisture sensors is the most economical way to control the cost [32; 34; 38; 39; 40; 52; 60]. However, as discussed for the perception layer in Section 3.1, solely relying on soil moisture sensors cannot guarantee reliability. A water meter sensor should be used to monitor the overall water changes [37; 55; 56; 57], but it cannot reflect the soil moisture situation. In addition, we need services such as calibration to ensure data reliability.  
[PERSON] et al. [96] designed an open-source application called \"SWAMP Kali\", which collects real data from all types of soil moisture sensors in real time to provide calibration services. \"SWAMP Kali\" uses stateless behavior, which does not store data in the application or a dedicated database. It uses data managed by Orion. Likewise, [PERSON] et al. [97] designed a tool called \"CalibrationTalk\" to handle incorrect placement or aging sensor issues. The tool has a special sensor called \"Wildcard\" to match sensors and provide calibration service.  
Sensors are normally put above the ground to receive signals. Sensors buried underground purposely avoid interfering with farm machinery [52]. [PERSON] et al. [52] proposed using an assisted IoT system to solve the problem. They put a Raspberry Pi on top of the sensor to convert it to a LoRa-enabled sensor and then bury it under the ground. The new sensor will receive the LoRa signal and store data on the Raspberry Pi to communicate with the drone. Once the drone flies over the farm in a predefined route, it can communicate with the LoRa-enabled sensor to collect soil moisture data. However, this proposal has increased the buried LoRa-enabled sensor price with the added microcontroller price, plus a drone in need, which will make the whole system cost extremely high.  
In addition to sensor types, the quantity of sensors also significantly affects the overall cost. In the network layer section, we mentioned LPWAN technology, which can be used to reduce the overall quantity used in the sensors. Nonetheless, so far, the LPWAN technology is still in the lab testing stage. Alongside security concerns (AES-128 bit, in the cyber-security area, is considered a low security level), narrow bandwidth means the same radio frequencies are required for the connected gateways. Thus, the coexistence of multiple gateways can create interference that affects performance. In addition, the farms are not flat; some even have steep slopes, which will also lower the performance of the LPWAN. However, if we use short-range protocols, such as WiFi, Bluetooth, or ZigBee, we need to add more sensors, since the range can cover up to 100 m.  
Table 7 lists the cost range for the user to choose from. The cost is based on ten sets of installed sensors plus the microcontroller. The base station, solar energy, or the user's mobile device are not included.