## 4 Discussion
### Recommendations
#### 4.2.2 Technology Related  
In [6], the creation of intelligent water quality solutions utilizing cutting-edge technology that provide real-time data access is essential for the management of water resources. Designing structures that are adaptable, modular, scalable, and simple for the user to install is essential. Better real-time monitoring tools that combine notifications and social media alerts are likely to be the subject of future research. Additionally, it is advised to use a mobile application to scan the water's colorization and monitor red tides using image processing [54]. Authors in [33] proposed a system that may be expanded in the future to include the creation of a software agent-based model for underground pipe health monitoring and consumption monitoring utilizing intelligent agents that will alert the SCADA engineer for prompt control action and supply restoration. This intelligent agent would introduce automation of control during crucial periods as a safeguard. The authors proposed  
Figure 4: Main recommendations.  
to concentrate on [57] evaluating and improving measuring procedures to increase the lifespan of sensor probes. Authors in [13] suggested that a Machine Learning technique can be expanded in the future to investigate the various physical characteristics of the water and forecast the location of a source of water experiencing an abrupt abnormal change in water quality, which can be useful in identifying and preventing contaminants from seeping into or being deposited into a source of water. The authors in [16] proposed to focus on a study in the future regarding the optimization of UAVs for sample WQ in rivers based on flying limits, power consumption, load, and river size in the usage of UAVs with LPWA communication technology. Miniature smart multi-parameter sensors for WQ will elevate the interest of study due to the load which plays a significant role.  
The usage of multi-criteria network-aware service composition algorithms and some pertinent access control systems can be used in the interim to ensure access control and boost service quality [36]. Additionally, utilizing deep learning, neural networks, and other artificial intelligence technology can be tried to create a smart data analysis scheme to evaluate and forecast water quality [36]. Authors in [60] discussed the implementation of a prediction algorithm, that might be utilized to use the analytical data to predict the sources of pollution in the water. More data should be gathered in the future to improve the AI prediction model they have created [7]. Additionally, more detection tools like NO2 and heavy metals, which are the main contaminants that harm consumers and fish farms should be included [7].  
Additionally, authors in [18] anticipated that their research can improve the development of wireless sensor network-based water quality monitoring within the context of environmentally friendly network operations. Also, they stated that proper discovery and exploitation of wireless energy transfer and optimization will enable sensors in a network used to monitor water quality to remain operational for an extended period. Authors in [64] elaborated that their research can be expanded to an automatic farm programmed in the future using the big data collected from the sensors, and we will use artificial intelligence to determine the best conditions for eels to grow. Additionally, it's crucial to safeguard wireless sensor data communication from interference [45].