# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### IoT Layer Dimension
#### 3.1.3 Application Layer
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Range** & **Technology** & **Paper IDs** & **Strength** & **Weakness** \\ \hline Short & WiFi & [31, 38, 41, 44, 45, 46, 58, 64, 55, 57, 58, 60, 64] & High data rate suitable for large message sizes. Embedded modules in Arduino Uno and (1 W) and limited range (up to 100 m). & High power consumption (1 W) and limited range (up to 100 m). \\ \hline Short & Bluetooth LE & [31, 58] & Moderate data rate compared to WiFi. Suitable for messages like sound. Embedded modules in Arduino Uno and Raspberry Pi. No additional modules are needed. Cost-effective. & Moderate power consumption (up to 500 mW) with a range of up to 100 m. Lower security (AES-128). \\ \hline Short & ZigBee & [32, 34, 53] & Energy efficient (1 mW), potentially increasing battery life. Cost-effective. & Moderate security (AES-128). Limited range (up to 20 m). \\ \hline Long & LoRaWAN & [39, 43, 52] & Low power consumption (25 mW) with a long range (up to 10 km). & Moderate security (AES-128). Narrow frequency bandwidth; potential interference issues. \\ \hline Long & NB-IoT & [37] & Low power consumption (17 mW). Extensive range (up to 15 km). & Moderate security (AES-128). Narrow bandwidth; potentially complex and costly to implement. \\ \hline Cellular & GPRS & [32] & Extensive range (up to 26 km). Compatibility with existing cellular plans. Broad bandwidth (850-1900 MHz). & High power consumption (1 W to 3 W). Dependency on cellular provider’s infrastructure. \\ \hline Cellular & 5G & [46, 54, 56, 57] & Long range (up to 28 km). Compatibility with current cellular plans. Extensive bandwidth (700 MHz-72 GHz). High data rates (20 Gbps) and enhanced security (256 bits). & High power consumption (1 W to 3 W). Requires cellular provider’s infrastructure. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wireless technology for IoT irrigation systems. CoAP facilitates direct device-to-device interaction. It empowers devices to uncover and negotiate compatible content formats autonomously. CoAP uses UDP (User Datagram Protocol). However, UDP does not guarantee message delivery or the preservation of message order. The choice between MQTT and CoAP depends on the precise demands of the IoT application. When devices need to exchange data adhering to fixed formats while ensuring reliable message delivery, MQTT could be a better choice. Conversely, if devices necessitate the ability to negotiate content formats and the application directly tolerates a degree of message loss or non-sequential arrival, CoAP becomes a more fitting selection. Throughout our study, as shown in Table 6, various platforms can be used to support IoT irrigation systems. For example, Bllyn and ThingSpeak are cloud-based platforms that use the MQTT IoT protocol. The Android API is popular for developing codes to facilitate communication with various sensors and establish connections with cloud platforms such as ThingSpeak [42; 64] or Blynk [41]. Notably, the preference has tilted toward open-source platforms over conventional IoT cloud platforms, including AWS IoT, Microsoft Azure IoT, and Google Cloud. The choice between ThingSpeak and Blynk hinges on the IoT application's specific requirements and intended functionalities. The recent surge in AI and big data technologies has led to enhanced decision making through AI-based predictors. Leveraging massive datasets and discernible trends, these predictors hold the potential to refine and fine-tune the accuracy of various processes. Some examples of AI include Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI) [31; 64; 76; 77]. Most AI used in smart irrigation systems are ANI, since they focus on handling one task such as predicting water usage. Machine learning (ML) is a subset of AI, which relies on two types of data: the training and test sets. The training data are used to develop the initial model, while the test data are employed to validate the model's accuracy and subsequently refine it. More data enhance