# Evaluation of LoRa Network Performance for Water Quality Monitoring Systems
## 4 Conclusions  
The objective of this study was to monitor water quality data in an urban environment using the LoRa network as the primary communication method. Real-time analysis of water quality data allows for a quick decision-making system for water environmentalists to prevent pollution outbreaks. Therefore, evaluating the LoRa network performance for developing an autonomous system signifies its adaptability to operate in an urban environment. A detailed analysis of the RSSI, SNR, packet loss, path loss, and received power demonstrated the LoRa compatibility for independent operation. Changes in the network properties of LoRa, such as SF and antenna gain, heavily impact the performance of the LoRa network. The increase in SF and antenna gain increases the distance, receiver sensitivity, and ability to overcome obstacles within the transmission line. The results can assist different configurations for environmental monitoring with an IoT-based LoRa network in urban settings. Furthermore, the large distance between the end-nodes can reduce the redundancy of packet data. The integration of observations of water quality data from the K-means method and packet loss can be used to analyze the anomaly deduction on instruments, changes in water quality properties, and network performance. Although LoRa is known for its capability to operate below \(-130\) dBm, enhancement of its network properties, such as bandwidth, can improve its efficiency in terms of its and data type.  
Finally, this study demonstrates the real-world performance of LoRa as a water quality monitoring communication system, highlighting its various network properties, within an urban area on campus. Future work can focus on the design of water stations and can be adjusted to provide more durable structures. Additionally, analysis of water quality sensor data with water quality data from laboratories can also be investigated further to attain higher accuracy in water categorization for each characteristic.  
Conceptualization, S.N.S.T.; methodology, S.N.S.T.; software, S.N.S.T.; validation, S.N.S.T.; formal analysis, S.N.S.T. and T.F.T.M.N.I; resources, M.S.A.T. and M.M.; data curation, S.N.S.T.; writing--original draft, S.N.S.T.; writing--review and editing, M.S.A.T., M.M., Z.H.A.H. and T.F.T.M.N.I.; supervision, M.S.A.T. and M.M.; project administration, M.S.A.T., Z.H.A.H. and T.F.T.M.N.I; funding acquisition, M.S.A.T. and Z.H.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Malaya, grant number Impact-Oriented Interdisciplinary Research Grant (IIRG006B) and Research University Grant (RU Faculty) GPF007A.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
Special thanks to the team of Collaborative IR4.0 Strategies for Lake and River Conservation Management using Innovative Sensing Platforms, the team of sub-programs for river conservation using the LoRa network, and the Water Warriors team for their ideas, knowledge, and material support.