A Systematic Literature Study on IoT-Based Water Turbidity Monitoring: Innovation in Waste Management ^†

Abstract

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity.

1. Introduction

Water quality monitoring is an important effort in preserving the environment [1], especially in areas affected by industrial activity [2]. Waste disposal without going through an adequate treatment process can cause harmful pollution [3], both for aquatic ecosystems and human health [4]. According to data from World Health Organization (WHO), about 2.2 billion people do not have access to safely managed drinking water, leaving them vulnerable to diseases such as cholera, diarrhea, and hepatitis A due to contamination with harmful bacteria, viruses, and chemicals. An estimated 1 million deaths occur each year due to contaminated water, especially among children under the age of five [5].

Water quality monitoring is not only important for environmental sustainability but also contributes greatly to improving public health. Previous research has shown that access to clean water is essential in preventing disease. For example, found that improved access to clean water and better sanitation significantly reduced the incidence of diarrhea among children under five years of age [6]. Similarly, research in Indonesia shows a significant relationship between access to clean water and the incidence of diarrheal diseases [7]. On a global scale, inadequate water, sanitation, and hygiene (WASH) was responsible for about 829,000 deaths from diarrhea in 2016, with 297,000 of these occurring in children under the age of five [8]. Most importantly, access to better water and sanitation can reduce the risk of diarrheal diseases by 24.5% in low- and middle-income countries [6]. Monitoring water quality is a crucial step in preventing public health problems and preserving the environment. By measuring parameters such as turbidity, pH, dissolved oxygen, temperature, nutrient concentration, as well as the presence of biological and chemical contaminants, we can get a clear picture of the condition of the water. This information is very important to formulate effective water resources management strategies, protect aquatic ecosystems, and ensure sustainable access to clean water for the community [5].

One of the main parameters used in water quality monitoring is turbidity [9]. Water turbidity shows the number of suspended solid particles that can have a direct impact on aquatic biota life, high levels of turbidity also interfere with the photosynthesis process of aquatic plants, and disrupt the balance of aquatic ecosystems [10]. The importance of turbidity monitoring lies not only in its ability to provide early indications of potential contamination, but also in its role as a signal for the presence of harmful contaminants [11], including pathogens, heavy metals, hazardous chemicals, and microplastics, which can cause negative impacts on human health and ecosystems [8]. The U.S. Environmental Protection Agency (EPA) recognizes turbidity as an important parameter for assessing water quality, especially in drinking water sources. Turbidity monitoring can provide insights into the effectiveness of waste management practices, as increased turbidity often correlates with runoff from urban areas, agricultural activities, and industrial disposal [12]. Thus, monitoring turbidity is a crucial step in efforts to maintain water quality and protect the environment and public health.

Internet of Things (IoT) technology has become a significant innovation in water quality monitoring, particularly in measuring turbidity levels [13]. IoT enables the integration of turbidity sensors that can collect and transmit data in real-time over the internet [14]. Through continuous data collection, IoT-based systems can provide a more comprehensive picture of the problem of water turbidity in various environments, especially in sewage disposal areas [15]. The data obtained can be processed to analyze water pollution trends, allowing for more precise and data-driven decision-making [16].

In line with this progress, several previous studies have shown the application of IoT technology in water quality management. Research conducted by Komang Try Wiguna Adhitya primantara designed a water and air quality monitoring system that provides real-time information about environmental conditions. The system is equipped with sensors to measure pH, temperature, turbidity, and Total Dissolved Solids (TDS) for water, as well as air pollutants such as carbon monoxide (CO) and nitrogen dioxide (NO2). The results show that users can access quality data through web and mobile applications, making it easier to understand the environmental conditions around them [17]. In line with that, research by Fernando Solano focuses on the development of IoT-based smart systems to monitor wastewater, with the aim of detecting illegal discharge in sewers. They proposed an anomaly detection architecture called Hop-by-hop Anomaly Detection and Actuation (HADA), which improves real-time anomaly detection by taking into account seasonal variations and sensor noise. The results show the effectiveness of the system in providing a better indication of illegal dumping through correlation analysis between pH, conductivity, and flow signals, as well as storing anomalous information for further analysis, improving response to contamination [18].

Furthermore, research by Mohamed Abdirahman Addow concerns the problem of drinking water quality in Somalia, especially related to the challenges faced in traditional water quality monitoring. The study noted that contaminated water quality is a serious threat to public health, and that existing monitoring methods are often inadequate to detect changes in real-time. The results show that the proposed IoT-based water quality monitoring system is capable of monitoring important parameters such as turbidity, conductivity, and pH in real-time, allowing for the rapid identification and handling of water quality issues. By providing early warning, the system has the potential to improve public health outcomes and water management in Somalia [19].

Then research by Yin Xu focuses on the development and evaluation of IoT-based portable water quality monitoring systems for aquaculture. The results show that this system is accurate, stable, and effective, providing a reliable and efficient solution for smart aquaculture management, especially for small and medium-sized enterprises. With the ability to wirelessly transmit data to a cloud platform, users can monitor water quality remotely through a visual interface, improving efficiency and responsiveness in aquaculture environmental management [20]. Furthermore, research by Ahmad Alshami explores the latest developments in Internet of Things (IoT) technology related to water and waste infrastructure management, as well as water quality monitoring. The focus of this research is to bridge the knowledge gap by analyzing 119 relevant articles, which identify publication trends and relationships between keywords in IoT advancements. The results demonstrate the challenges in the application of this technology and provide valuable insights for further research and development in effective water and waste management practices [21].

The research aims to conduct a systematic literature review on IoT-based innovations in river turbidity monitoring, with a focus on waste management. This study explores various approaches and the latest technologies applied in IoT-based water quality monitoring systems. Unlike previous research, which primarily examines technical aspects or specific cases, this study provides a comprehensive review that integrates innovations, implementation challenges, and technological advancements. By addressing these aspects holistically, this research aims to offer a broader and deeper perspective on how IoT can enhance the effectiveness of waste management and contribute to the preservation of water resources from pollution.

This research has a high urgency considering the challenges in water quality management that are increasingly complex. The increase in pollution due to industrial activities and rapid urbanization make water quality monitoring increasingly important to protect public health and aquatic ecosystems. If this research is not carried out soon, the opportunity to adopt innovative solutions in water quality monitoring systems can be missed. Without a deep understanding of the application of IoT technology, water quality problems that threaten health, especially in vulnerable areas, will continue. Uncertainty in water quality management can increase the risk of water-related diseases and lower quality of life. Therefore, this research is not only relevant, but also urgent to ensure environmental sustainability and community welfare.

2. Materials and Methods

The method used in this study is Systematic Literature Review (SLR) with the PRISMA (Preferred Reporting Items for Systematic Reviews) protocol. The researcher formulated the research questions clearly to ensure that the literature review was directed and systematic. This question is designed to identify important aspects in IoT-based water turbidity monitoring as well as evaluate its effectiveness, advantages, and challenges in its application

• RQ1: How can the integration of IoT in waste monitoring systems improve the efficiency of liquid waste treatment?
• RQ2: How significant is IoT-based monitoring compared to traditional methods in early detection of wastewater pollution?
• RQ3: What are the challenges and solutions in the implementation of IoT systems for industrial and domestic waste management?

Through this analysis, it is hoped that a more optimal strategy can be found in monitoring water turbidity to support the sustainability of water resource quality.

In the literature search process, the researcher found 222 articles relevant to keywords (“IoT” OR “Internet of Things” OR “Wireless Sensor Network”) AND (“water quality” OR “water pollution” OR “turbidity”) AND (“wastewater” OR “sewage” OR “effluent treatment”) in the Scopus database. Prior to the screening, duplication was removed, resulting in 220 articles for the initial selection stage. Furthermore, screening was carried out by limiting the range of publication years between 2020 and 2025, resulting in 188 articles. Further filtering by field of study, which only includes Computer Science and Engineering, leaves 134 articles. After being re-filtered by document types that only include articles, the number is reduced to 31 articles. Finally, by applying an open access filter, 20 articles were obtained that met all the criteria for further analysis. The literature selection process was carried out through the stages of identification, screening, eligibility assessment, and inclusion of relevant studies, as illustrated in the PRISMA flow diagram Figure 1.

To ensure the quality and relevance of the selected articles, this study applies inclusion and exclusion criteria in the selection process. The inclusion criteria include articles from Scopus indexed journals or conferences, as well as specifically discussing the application of IoT in water turbidity monitoring for waste management. In addition, only articles with full text are considered for analysis. Exclusion criteria were applied to filter out less relevant articles, including studies that only discussed IoT for drinking water with no link to waste, articles that focused only on turbidity sensors with no implementation in waste management systems. Additional filters are applied based on the number of citations >5, so that only articles that have had a stronger academic influence and have been cited by other studies are considered. Of the 20 articles that were screened, only seven articles met the final criteria for further analysis.

3. Results

The results of this study are prepared to answer previously formulated research questions. The article selection process uses the PRISMA method, resulting in seven articles that are selected as the main reference. These articles are presented in

Table 1. Review results.

No.	Title	Author	Journal	Year
1.	A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible? [22]	Alessandro Moretti, Heidi Lynn Ivan, Jan Skvaril	Journal of Water Process Engineering	2024
2.	Automated Drone-Delivery Solar-Driven Onsite Waste water Smart Monitoring and Treatment System [23]	F. He, Ming Zhu, Jiawei Fan, Edwin Ma, Shengjie Zhai, Hui Zhao	Advancement of science	2023
3.	An Industrial Cloud-Based IoT System for Real-Time Monitoring and Controlling of Wastewater [24]	Ranya M. M. Salem, M. S. Saraya, Amr M. T. Ali-Eldin	IEEE Access	2022
4.	IoT Innovations in Sustainable Water and Wastewater Management and Water Quality Monitoring: A Comprehensive Review of Advancements, Implications, and Future Directions [21]	Ahmad Alshami, Eslam Ali, Moustafa Elsayed, A. E. Eltoukhy, Tarek M. Zayed	IEEE Access	2024
5.	New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation [25]	J. M. Jiménez, L. Parra, Laura García, Jaime Lloret, P. V. Mauri, P. Lorenz	Applied Sciences	2021
6.	Water Pollution Prediction Based on Deep Belief Network in Big Data of Water Environment Monitoring [26]	Li Liang	Scientific Programming	2021
7.	IoT-Based Water Monitoring Systems: A Systematic Review [27]	C. Z. Zulkifli, Salem Garfan, M. Talal, A. Alamoodi, Amneh Alamleh, Ibraheem Y. Y. Ahmaro, Suliana Sulaiman, Abu Bakar Ibrahim, B. Zaidan, A. Ismail, O. Albahri, A. Albahri, C. F. Soon, N. H. Harun, Ho Hong Chiang	Water	2022

to facilitate data analysis and interpretation.

4. Discussion

This discussion describes the results of the research with a focus on the answer to each research question (RQ). The discussion will be presented in the following

Table 2. Discussion.

No.	RQ1	RQ2	RQ3
1.	The integration of IoT in waste monitoring systems improves treatment efficiency by enabling real-time monitoring. This allows for a quick response to changing conditions, so that the processing process can be optimized more effectively. The technology also supports an automated control system that can dynamically adjust processes based on real-time data. Thus, the need for manual intervention is reduced, operational costs can be reduced, and the possibility of errors is minimized. In addition, IoT helps with energy optimization and resource recovery, transforming sewage treatment facilities into more efficient water resource recovery systems. With the utilization of this technology, waste treatment not only focuses on disposal but also on the reuse of valuable resources.	In terms of monitoring, IoT enables early detection of pollution through continuously updated data. This is an advantage over traditional methods that still rely on laboratory testing, which is often time-consuming and does not provide an instant response to changes in water quality. In terms of cost, IoT systems are more efficient because they can operate in-line or on-line. This makes it more cost-effective than conventional methods that are more expensive and often do not meet regulatory standards, reducing the burden of long-term monitoring costs.	However, the main challenges in IoT adoption include data security. The increasing interconnectedness of devices also increases the risk of data leaks, so strong security measures, such as encryption and secure protocol-based authorization systems, are needed. Scalability and system maintenance are also challenges that need to be considered. The solution to this problem is to develop a standard protocol that allows for easier integration and management of IoT devices and improves the sustainability of the system in the long run. In addition, the integration of IoT with conventional waste management systems can be complex. Therefore, a phased approach and training for the workforce are needed to ensure a smooth transition and optimal use of technology.
2.	The integration of IoT in waste monitoring systems allows for real-time data collection and analysis, thereby improving the efficiency of waste treatment. Sensors are used to monitor water quality parameters such as total dissolved solids (TDS), pH, and dissolved oxygen (DO) continuously. By applying machine learning algorithms, the system can predict water quality trends and optimize treatment strategies. This ensures more timely interventions as well as more efficient resource management.	IoT-based monitoring systems provide a significant advantage over traditional methods by enabling automated and continuous data collection. This supports the early detection of pollution, which is crucial in preventing the wider impact of pollution. Conventional methods often rely on periodic sampling and manual analysis, which can lead to delays in responding to pollution incidents. In contrast, IoT systems can provide real-time updates and alerts, allowing for faster decision-making and corrective action.	One of the key challenges in implementing IoT systems for industrial and domestic waste management is ensuring long-term stability and reliability, especially in remote or hard-to-reach locations. Issues such as power loss and sensor maintenance can hinder system performance. To address these challenges, the proposed solution includes the use of solar-powered systems that reduce energy consumption and improve sustainability. In addition, the implementation of a grid mapping system for the efficient placement of monitoring units ensures wider coverage of the pollution area with minimal resources.
3.	The integration of IoT in waste treatment systems allows for dynamic, continuous, and real-time monitoring of waste parameters such as pH and temperature. With this capability, the system can respond directly to detected anomalies, thereby improving the efficiency of the waste treatment process. Through a cloud-based model, data from various sensors can be accessed remotely, allowing for better decision-making and timely intervention. In addition, the system can automate valve controls to divert waste to the appropriate treatment facility, prevent damage, and ensure only the appropriate waste enters the treatment plant.	IoT-based monitoring systems significantly improve the early detection of waste pollution compared to traditional methods that rely on manual sampling and laboratory testing. Conventional methods are not capable of providing the continuous monitoring necessary to meet modern standards. The proposed system can also send SMS notifications to administrators when industrial waste is detected, ensuring that any issues can be addressed immediately. This is a major advantage over conventional monitoring techniques that often experience delays in response.	One of the main challenges in implementing IoT systems for waste management is the integration of various sensors and devices into one unified monitoring system. This requires careful planning as well as investment in compatible technology. In addition, the security and reliability of data transmission is a crucial factor, because security threats can interfere with the monitoring process. Applicable solutions include the use of strong encryption methods as well as regular system updates to protect against potential cyberattacks. Another challenge is the maintenance of IoT infrastructure which can require large resources. However, implementing a centralized platform for data management can simplify operations and reduce the workload of the management team.
4.	Real-time monitoring with IoT allows for continuous monitoring of the quality of liquid waste and its treatment efficiency. By detecting inefficiencies or malfunctions early, these systems can improve operational efficiency while preventing health and environmental risks. The use of IoT sensors improves data collection and analysis in wastewater treatment processes. The data collected allows for more accurate and timely data-driven decision-making. IoT also allows remote monitoring and control of waste treatment infrastructure, such as pumps and valves. This improves the efficiency of the system while reducing reliance on human intervention.	Early detection of pollution becomes more effective with IoT systems, which provide real-time data on water quality parameters such as pH and turbidity. This is far superior to traditional methods that rely only on periodic sampling and laboratory analysis. The integration of various sensors in IoT systems allows for more thorough monitoring of wastewater conditions. In contrast to conventional methods that often only measure certain parameters, IoT allows for more comprehensive analysis to identify various potential problems. Communication protocols such as Sigfox and Zigbee improve sensor connectivity in IoT systems. With this technology, data transmission becomes more reliable, so early notification of potential contamination can be carried out faster and more accurately.	One of the key challenges in the application of IoT for wastewater management is ensuring the accuracy of the sensors. Sensor reliability is essential to ensure the quality of the data used in decision-making. Energy optimization is also an important factor in IoT systems, given that many devices must operate in the long term. Efficient use of energy resources can improve the reliability and durability of monitoring devices. Communication reliability in IoT systems is also a challenge, especially in remote areas that have limited network access. A stable connection is necessary so that data can be sent without interruption and decisions can be made in real-time. To address these challenges, collaboration is needed between various stakeholders, including technicians, environmental scientists, and policymakers. With good cooperation, technical challenges can be overcome, and systems can be designed more optimally. In addition, the implementation of integrated systems with various types of sensors as well as advanced data analysis technology can improve the effectiveness of waste monitoring.
5.	The application of IoT in wastewater treatment improves efficiency by providing real-time water quality monitoring. This technology detects early levels of high salinity and contaminants such as oil spills, which are crucial for rapid intervention. IoT systems use a network of wireless sensors that can detect, locate, and track contaminants, providing a comprehensive picture of the conditions of the aquatic environment. This approach allows for the optimization of the treatment process before the water is distributed for irrigation.	IoT-based monitoring systems are more effective than traditional methods in detecting wastewater pollution early. With continuous monitoring without the need for human supervision, this system overcomes the limitations of conventional methods. The physical sensors in these systems detect and analyze changes in water quality in real-time, making them ideal for smart irrigation systems. This capability allows for a faster response to pollution events than conventional monitoring techniques.	One of the key challenges in the application of IoT for industrial and domestic waste management is ensuring the reliability and accuracy of sensor data. A reliable monitoring network is needed to detect contamination effectively. Applicable solutions include the utilization of advanced data analysis algorithms and artificial intelligence techniques to improve the accuracy of pollution detection and predict its movement. In addition, the application of optimization algorithms such as NSGA-II improves the effectiveness of sensor placement, maximizes detection, and reduces redundancy.
6.	The integration of IoT in waste monitoring systems allows for real-time data collection and analysis, thereby improving the efficiency of wastewater treatment. By using wireless sensor nodes, the system can continuously monitor wastewater quality and detect changes in real time. This allows for timely intervention as well as optimization of the processing process. The integration of big data technology with IoT also improves the accuracy of monitoring waste treatment equipment, ensuring optimal operational conditions.	IoT-based monitoring systems provide early detection of wastewater pollution that is superior to traditional methods. Conventional methods rely on manual sampling and laboratory analysis, which often leads to delays in response. In contrast, IoT systems can continuously monitor and transmit data to a cloud platform for immediate analysis, thereby accelerating the identification of pollution sources. Studies show that IoT technology is able to detect pollution sources with an accuracy of no less than 70%, proving its superiority over conventional methods.	The main challenge in the application of IoT systems for industrial and domestic waste management is the large-scale management of data generated by various sensors. To overcome these challenges, advanced data analytics and a reliable technical infrastructure are needed to ensure efficient data management and interpretation. Ensuring that the prediction model remains reliable and accurate is also a crucial aspect. Deep learning techniques, especially Deep Belief Networks (DBNs) optimized with particle swarm optimization, can improve prediction accuracy and overcome these challenges. The integration of different types of sensors is also a challenge, as some sensors are more effective at detecting certain pollutants than others. A hybrid approach that combines specific sensors and versatile sensors can be an effective solution in increasing the effectiveness of monitoring systems.
7.	The integration of IoT in wastewater monitoring systems can significantly improve treatment efficiency by enabling continuous monitoring and real-time data acquisition. This allows for direct detection of pollutants as well as timely intervention, making the treatment process more effective. IoT systems can also automate the monitoring of crucial water quality parameters, reducing the need for manual sampling and analysis that is often time-consuming and inefficient.	IoT-based monitoring systems are more effective than traditional methods in detecting wastewater pollution early. Conventional methods that rely on periodic sampling risk missing transient pollution events. In contrast, IoT systems allow for continuous monitoring, so responses to pollution incidents can be carried out more quickly. The ability to collect and analyze data in real-time also improves the decision-making process, so that pollution risk management and mitigation can be carried out more optimally.	The application of IoT systems in industrial and domestic wastewater management faces several challenges, such as difficulties in accurate and real-time measurement of various pollutants, the complexity of water resource management due to rapid population growth and resource limitations, and the transition from conventional monitoring methods that are still constrained by infrastructure and are resistant to change. To address these challenges, the integration of advanced technologies such as artificial intelligence, machine learning, and fuzzy logic with IoT can improve the accuracy and efficiency of water quality monitoring systems. In addition, the development of user-friendly interfaces as well as training for related personnel can accelerate the adoption of IoT systems in both industrial and domestic environments.

:

5. Conclusions

IoT-based water turbidity monitoring provides significant innovations in waste management by improving the efficiency, accuracy, and responsiveness of monitoring systems. By utilizing wireless sensors and cloud-based technology, the system enables real-time monitoring of water turbidity parameters, which is crucial in detecting early contamination. The ability to collect and analyze data on an ongoing basis allows for faster and more precise decision-making, allowing the waste treatment process to be optimized more effectively. The main advantage of IoT-based monitoring systems over conventional methods lies in their ability to detect changes in water quality instantly, without the need for time-consuming manual sampling. With the integration of artificial intelligence and machine learning algorithms, this system can predict the trend of water turbidity. In addition, the implementation of a reliable sensor network and communication protocol ensures stable data transmission, so that monitoring can be carried out accurately and efficiently.

Despite offering many benefits, the application of IoT in water turbidity monitoring still faces challenges. Key challenges include data security, sensor reliability, and system scalability and maintenance, especially in industrial environments and remote areas. The risk of data leakage due to high device connectivity requires the implementation of strong security protocols, such as data encryption and secure protocol-based authentication. In addition, the transition from conventional methods to IoT-based systems requires a gradual approach as well as workforce training to ensure that technology adoption can run optimally. Overall, IoT-based water turbidity monitoring offers innovative solutions in waste management by improving the effectiveness of pollution detection and optimization of water treatment processes. By continuing to develop technology and overcome existing challenges, this system has the potential to become a new standard in more efficient, accurate, and sustainable water quality monitoring.