Aug 2026· Journal of Environmental & Earth Sciences· pp. 49-79· 0 citations· 122 references
TL;DR
How the Internet of Things (IoT-based) sensing and artificial intelligence analytics may be combined in order to facilitate real-time monitoring and optimization in industrial treatment trains, including pretreatment and biological treatment systems, membranes, and zero-liquid-discharge (ZLD) systems is reviewed.
Abstract
Smart industrial water treatment is progressively sought after to achieve stricter discharge and product-water requirements, decreased energy and chemical use, and enhanced stability at fluctuating influent and working regimes. This article provides a review of how the Internet of Things (IoT-based) sensing and artificial intelligence analytics may be combined in order to facilitate real-time monitoring and optimization in industrial treatment trains, including pretreatment and biological treatment systems, membranes, and zero-liquid-discharge (ZLD) systems. The initial contextual framework (smart treatment) is placed on industrial performance goals and limitations, focusing on partial observability, sensor pollutions/fouling, measurement delay, and multi-objective optimization amongst compliance, cost, particle recovery objectives, and asset health. We next consider IoT and architectures of sensing used to ensure trustworthy monitoring, such as time-based pipelines of data, edge cloud pattern of installation and engineering of data quality, which is used to validate, redundancy, and fault detection. It is based on these backgrounds that we consider artificial intelligence (AI) techniques in anomaly detection, fault diagnosis, soft sensing, and probabilistic forecasting, and point out how regime awareness, explainability, and uncertainty quantification must be applied to risk-sensitive operations. Prescriptive capabilities are considered in a control maturity perspective, between decision support and constrained supervisory and closed-loop control. We contrast classical methods, e.g., model predictive control, with data-based optimal control, e.g., Bayesian optimization, safe/offline reinforcement learning, and explain why digital twins can be used to enable validation and operator training. Last but not least, we discuss deployment facts-OT/IT (operational technology/information technology) integration, cybersecurity, lifecycle management, and human factors, and offer a vision of the future based on interoperable data models, strong cross-site transfer, and optimization that is proven to be safe.
Rising industrialization and urbanization have significantly impacted water quality, making effective monitoring essential. Traditional monitoring systems are often expensive, time-consuming, and lack real-time capabilities, especially in developing regions. This paper proposes a low-cost, IoT-based water quality monitoring system that continuously tracks key parameters such as pH, turbidity, temperature, and dissolved oxygen. The system uses affordable sensors, microcontrollers, and wireless communication to enable real-time data collection and transmission. Its modular and scalable design allows deployment even in rural and resource-limited areas. Data collected from multiple sensing nodes is sent to a cloud platform for storage and analysis. Continuous monitoring helps in early detection of contamination, while data analytics supports anomaly detection and improved decision-making. The study reviews existing systems and highlights their limitations, including high cost and lack of scalability. Experimental results show that the proposed system provides reasonably accurate measurements compared to standard laboratory equipment. Overall, the system offers a cost-effective and efficient solution for real-time water quality monitoring, with future scope for integrating machine learning and large-scale smart city applications.
G. Kézdi, Jurgen Willman· International Journal of Mod...· 0 citations
Smart water distribution systems play a vital role in addressing modern water management challenges caused by urbanization, population growth, industrial expansion, and climate change. Traditional water supply networks often suffer from high water losses, inefficient monitoring, delayed fault detection, and increased operational costs due to manual management practices. To overcome these limitations, smart water management integrates real-time sensor networks, Internet of Things (IoT) technologies, wireless communication, cloud computing, and data analytics. Advanced sensors continuously monitor critical parameters such as water pressure, flow rate, water quality, leakage, temperature, pH, and contamination levels across pipelines, reservoirs, treatment plants, and consumer endpoints. This study examines the architecture, communication mechanisms, sensing technologies, and optimization techniques used in smart water distribution systems. The proposed framework employs layered deployment of pressure, flow, and water-quality sensors combined with cloud-based analytics and predictive control algorithms. Wireless communication technologies such as ZigBee, LoRaWAN, GSM, and Wi-Fi enable efficient data transmission across distributed infrastructure. The system supports real-time leakage detection, pressure regulation, contamination monitoring, predictive maintenance, and energy-efficient pump scheduling. Simulation results demonstrate significant improvements in leakage detection accuracy, operational efficiency, water conservation, energy savings, and infrastructure reliability compared to conventional distribution networks. The findings indicate that smart water distribution systems powered by real-time sensor networks can transform traditional water utilities into intelligent, adaptive, and sustainable platforms, supporting resilient water management and future smart city development.
James Carter, Patricia Hall· International Journal of Mod...· 0 citations
In Malaysia, the degradation of water bodies due to rapid urbanisation, agricultural runoff, and industrial waste highlight the urgent need for continuous, efficient water quality monitoring. However, current manual-based sampling methods are time-consuming, labour-intensive, and insufficient for real-time assessment, thus limiting proactive intervention. In response to these limitations, this study aims to design an integrated water quality monitoring system that focuses on four parameters which are Dissolved Oxygen (DO), pH, Temperature, and Turbidity. These parameters selected for their importance in assessing aquatic health and pollution levels. To achieve this, the system design incorporates Arduino as the central microcontroller for sensor integration, coupled with Long Range (LoRa) communication for energy-efficient, long- range data transmission. Sensor data are envisioned to be transmitted to a cloud- based dashboard for real-time monitoring and analysis. The proposed design is expected to lay the groundwork for a scalable and cost-effective solution that enhances monitoring accessibility, reduces reliance on manual sampling, and supports proactive water resource management aligned with Sustainable Development Goal (SDG) 6: Clean Water and Sanitation.
N. A. Makin, A. Azahari, A. M. Firdaus et al.· Journal of Engineering and T...· 0 citations
The growing threats to river water quality demand innovative approaches for effective monitoring and protection. This paper proposes an Internet of Things (IoT)-based river water quality monitoring system to address this challenge. The proposed system utilizes a network of sensors strategically deployed within the river, measuring crucial parameters like temperature, pH, dissolved oxygen, turbidity, and conductivity. Sensor data is continuously transmitted wirelessly to a central hub for processing and analysis. Utilizing cloud computing platforms, the system enables real-time data visualization and analysis, allowing for prompt identification of potential pollution events. Additionally, the system integrates alerting mechanisms to notify relevant authorities, facilitating timely interventions. This paper presents the design, implementation, and field testing of the proposed system, along with a thorough evaluation of its performance. The results demonstrate the system's effectiveness in capturing comprehensive water quality data, facilitating real-time monitoring, and enabling proactive water management strategies.
E. Amrutha, Dinesh Ram S P, Ranil Vikram P· International Journal of Lat...· 0 citations
This paper presents the design, development and evaluation of an Internet of Things (IoT)-based smart waste management system aimed at addressing the inefficiencies and public health concerns associated with traditional waste collection methods. The proposed system integrates ultrasonic sensors for real-time bin fill-level detection, a rain sensor for environmental compensation, an ESP32 microcontroller for data processing and transmission, and a dual-mode feedback mechanism via a local LCD display and a remote web dashboard. Experimental results demonstrate a 99% accuracy in fill-level and rain detection, with data updates to the cloud platform (ThingSpeak) occurring within 2 seconds of a 15-second reporting interval. The system's robustness, low cost, and energy efficiency highlight its potential for scalable deployment in urban environments, contributing to cleaner, smarter communities.
O. Saeed, Ayesha Noroz, Maimoona Waqar et al.· Natural and Applied Sciences...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
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