# Remote Water Quality Monitoring System for Use in Fairway Applications
## 4 Conclusions  
The presented water quality measurement system with IoT-based data transmission yields several important conclusions:  
* Effectiveness of IoT Technology in Water Quality Monitoring: Wireless IoT-based systems are effective tools for the continuous monitoring of water quality parameters.  
Figure 10: Temperature variation over time.  
The system detailed in this article demonstrates that IoT technology enables rapid response to changes in water quality and identification of potential threats.
* Advantages of Wireless Data Transmission: Wireless data transmission allows for immediate access to information regarding water conditions without the need for physical intervention. This enhances the efficiency of water resource management and minimizes operational costs.
* Faster Response to Threats: Immediate notifications of irregularities or exceedances of established norms facilitate quick responses and reduce risks to public health.
* Long-Term Trend Analysis: The system enables data collection over extended periods, allowing for trend analysis and identification of long-term changes in water quality.
* Challenges and Future Development Opportunities: Despite numerous benefits, challenges related to data security and the integration of various IoT systems persist. Future development will focus on refining data analysis algorithms and enhancing system resilience against failures.  
Prospects for Future Applications: IoT technologies will play an increasingly significant role in monitoring and protecting water resources worldwide. Innovative solutions may include leveraging artificial intelligence for predicting changes in water quality and automating purification processes.  
The implementation of wireless water quality monitoring systems in IoT is a crucial step toward sustainable water resource management. Continued research is essential for improving measurement accuracy and expanding the applicability of IoT technologies in the water sector. The authors will focus future work on the following:  
* Development of an autonomous buoy:
* Finalizing the design of an autonomous buoy made from innovative materials, ensuring durability, resistance to harsh environmental conditions, and potential collisions.
* Expanding the power supply system with alternative energy sources to support the long-term operation of the device without frequent maintenance, especially for water bodies with low exposure to risks.
* Advanced data analytics:
* Implementing machine learning and artificial intelligence algorithms for predictive analysis of water quality trends.
* Developing anomaly detection systems to automatically identify unusual patterns or sudden changes in water quality.
* Scalability and Integration:
* Ensuring compatibility with other IoT networks and environmental monitoring systems to create a comprehensive ecosystem approach.
* Expanding the system's ability to monitor multiple water bodies simultaneously.
* Field testing: Conducting extensive field tests in real-world conditions to verify the system's performance under varying circumstances.  
The proposed future work demonstrates the system's potential for broad application while addressing current limitations.  
Conceptualization, M.S., P.B., E.K., D.T. and J.W.; methodology, M.S., P.B. and D.T.; software, M.S.; validation, P.B. and D.T.; formal analysis, E.K. and J.W.; investigation, M.S. and P.B.; resources, M.S., P.B., E.K., D.T. and J.W.; data curation, M.S.; writing--original draft preparation, M.S., P.B. and D.T.; writing--review and editing, M.S., P.B., E.K., D.T. and J.W.; visualization, M.S.; supervision, D.T.; project administration, E.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.