The degradation of water quality has become a critical concern worldwide, necessitating innovative approaches for monitoring and predicting water quality. This paper proposes an integrated framework that combines the Internet of Things (IoT) and machine learning paradigms for comprehensive water quality analysis and prediction. The IoT-enabled framework comprises four modules: sensing, coordinator, data processing, and decision. The IoT framework is equipped with temperature, pH, turbidity, and Total Dissolved Solids (TDS) sensors to collect the data from Rohri Canal, SBA, Pakistan. The acquired data is preprocessed and then analyzed using machine learning models to predict the Water Quality Index (WQI) and Water Quality Class (WQC). With this aim, we designed a machine learning-enabled framework for water quality analysis and prediction. Preprocessing steps such as data cleaning, normalization using the Z-score technique, correlation, and splitting are performed before applying machine learning models. Regression models: LSTM (Long Short-Term Memory), SVR (Support Vector Regression), MLP (Multilayer Perceptron) and NARNet (Nonlinear Autoregressive Network) are employed to predict the WQI, and classification models: SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), Decision Trees, and Random Forest are employed to predict the WQC. Before that, the Dataset used for evaluating machine learning models is split into two subsets: Dataset 1 and Dataset 2. Dataset 1 comprises 600 values for each parameter, while Dataset 2 includes the complete set of 6000 values for each parameter. This division enables comparison and evaluation of the models’ performance. The results indicate that the MLP regression model has strong predictive performance with the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values, along with the highest R-squared (0.93), indicating accurate and precise predictions. In contrast, the SVR model demonstrates weaker performance, evidenced by higher errors and a lower R-squared (0.73). Among classification algorithms, the Random Forest achieves the highest metrics: accuracy (0.91), precision (0.93), recall (0.92), and F1-score (0.91). It is also conceived that the machine learning models perform better when applied to datasets with smaller numbers of values compared to datasets with larger numbers of values. Moreover, comparisons with existing studies reveal this study’s improved regression performance, with consistently lower errors and higher R-squared values. For classification, the Random Forest model outperforms others, with exceptional accuracy, precision, recall, and F1-score metrics.

Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality

Within Intelligent Transportation Systems (ITSs), modeling of traffic flow assumes a pivotal role, as it is crucial for alleviating traffic congestion and reducing carbon emissions. Developing a reliable and sustainable model for predicting traffic flow is highly challenging due to the fluctuations and nonlinear characteristics of traffic flow. In addition to being a practical problem, accurately forecasting traffic flow provides substantial challenges to the researchers working in this area. The aim of this study is to develop a complex framework for forecasting short-term traffic flow with the purpose of improving prediction accuracy. In order to attain this goal, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with a Modified Firefly Optimization Algorithm (MFOA) known as MFOA-Bi-LSTM has been proposed. The MFOA optimization technique has been utilized to optimize the hyperparameters of the deep learning models. Moreover, the optimization of the Bi-LSTM network prediction model is achieved through the application of the MFOA technique, which is renowned for its ability to converge quickly, maintain robustness and perform extensive global searches. To better capture complex patterns in the input data and increase prediction accuracy, model's architecture makes use of the bidirectional capabilities of Bi-LSTM layers. This combination of Bi-LSTM and FOA is proposed for optimizing complex sequence modeling tasks where selecting the right hyperparameters for the model is challenging and it allows for automated hyperparameter tuning and often leads to improved model performance. In terms of performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Correlation Coefficient (r), the proposed model significantly outperforms the other selected models and study findings confirm its appropriateness for the predictive task. 

MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction

The world's population growth, fast industrial development, and the birth of civilization are all driving up demand for clean water. Fluoride (F-) intake in excess of 4 mg/L is categorized as harmful to human health. Layered double hydroxide (LDH) is a well-known F- removal material due to its high defluoridation capacity and low cost. This work offers a review of fluoride removal methods, absorbent development, characterization techniques, and ideal LDH conditions for fluoride removal in drinking water. The most important details are the Industrial Revolution 4.0 (IR4.0) technologies used in wastewater treatment, the transformation of the circular economy, and the need for safety and risk assessment to ensure the industry's key performances are maintained. By embracing the IR4.0 concept and implementing the Sustainable Development Goals (SDG), wastewater treatment facilities could improve their operational efficiency while also ensuring improved safety standards and compliance with environmental regulations. 

Fluoride removal in drinking water using layered double hydroxide materials: Preparation, characterization and the current perspective on IR4.0 technologies

IoT Innovations in Sustainable Water and Wastewater Management and Water Quality Monitoring: A Comprehensive Review of Advancements, Implications, and Future Directions.

In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy.

Distributed Bayesian Inference for Large-Scale IoT Systems

Adaptive hyperparameter optimization for short term traffic flow prediction with spatial temporal correlated raster data

The emergence of the Internet of Things is expected to produce a huge amount of data streams for sensing the phenomenon from the real world. These data patterns i.e. data distributions keep on evolving and are subject to change based on the changes in the external environment. Valuable and useful information should be extracted from this data stream in real-time- to find out the events of interest. Pattern extraction from stored data and its analytics is convenient, though stream analytics has major challenges associated with it like identifying the event patterns from limited data in the stream, concept drift detection, outlier detection, and handling, limited storage, and processing capacity etc. Traditional methods, in general, work well with historical data yet have limitations while dealing with real-time data streams. Hence, the present work proposes adaptive clustering-based Event Detection Scheme for IoT (AEDS-IoT) stream data, which can infer events of interest from data distribution patterns present in streaming data. Initially, the scheme uses an adaptive window to collect the streaming data and processes it to detect the events. Further, the event patterns are detected by adaptive clustering. After identifying event patterns, the user assigns labels based on spatial–temporal conditions. The proposed AEDS methodology updates the patterns generated from upcoming stream data received. The performance of the proposed scheme is compared against the state of the scheme using the Arhus dataset. To corroborate the scheme with experimental evidence, the scheme was applied to the data collected from the Ganga river(the life line of North India) using smart water sensors. The performance and efficacy of AEDS-IoT were also tested on these data to detect upcoming event patterns from the data stream. 

AEDS-IoT: Adaptive clustering-based Event Detection Scheme for IoT data streams

Stress is a mental illness that impacts facets of life and can cause crucial health problems like sleep disorders and depression. In order to stay informed about one’s mental health, it is important to analyze one’s vitals. Wearable IoT devices collect and send the physiological parameters to an edge device for further processing and monitoring stress levels. Additionally, person-specific stress monitoring systems outperform generic ones, but they have limitations because person-specific models are not very adaptive to a wide range of people. Furthermore, creating a generic stress model is difficult because of different stress handling capacities. In this paper, we have proposed an IoT and Machine learning-based stress monitoring system. The proposed approach is a hybrid model that gives a relatively accurate prediction. We have demonstrated the proposed model on the WSEAD dataset and comparative analysis has been done with state-of-the-art methods.

A Hybrid Approach based Stress Monitoring System for Office Environment using IoT

Internet of Things (IoT) devices are constantly producing vast amounts of data, necessitating efficient storage and processing to extract useful information. However, the models used to extract relevant information from IoT data are often hindered by the lack of useful data and the ever-changing distribution of this data. This paper introduces an incremental data clustering technique on a continuous stream of data through a Dirichlet process-based approach that is adept at handling the formation of clusters in streaming data. The complete approach is twofold; firstly, it starts with an estimated distribution of data and allocates an incoming data point to the estimated data distribution. Secondly, it refines the estimated data distribution after the allocation of the current point and over the subsequent arrival of data points. The influx of data leads to greater challenges in determining clusters for incoming points and preserving the current clusters for improved decision-making. In this context, our proposed approach deals with the increasing amount of data using a selective elimination technique on both existing and incoming data. To assess the performance of the proposed approach, benchmark experiments have been performed using benchmark datasets. The results of the experiments demonstrate that the proposed model has a gain ranging from 2% to 4% as compared to the existing state-of-the-art and recent adaptive clustering approaches in terms of clustering accuracy with incremental data addition and variable clustering parameters. The proposed method shows a high gain in terms of running time ranging from 2% to 20% as compared to the existing approaches depending on the data reduction parameter. Furthermore, research findings through this work indicate that it is possible to set a trade-off between accuracy and running time by adjusting the elimination parameter depending on the requirements of the considered application.

KIHCDP: An Incremental Hierarchical Clustering Approach for IoT Data Using Dirichlet Process

IoT data analytics have numerous applications that generate huge data to gain new insights and information. How-ever, this work remains challenging due to the heterogeneity of IoT data sources, unnecessary data processing, uncertainty in decision-making, data biasness, and ever-increasing data size. To overcome these challenges, we propose distributed decision fusion framework for the large-scale IoT ecosystem. The proposed framework has divided into three-level. The first and second level provides the local decision of the small individual ecosystem using the filter method-based feature selection and dynamic classifier selection criteria for decision making; whereas the third level fuses the collected decision from the small ecosystems using Majority voting, Weighted majority voting and distributed Naive Bayes classifier. Lastly, we illustrate performance of the proposed solution on the US-Accidents dataset.

Distributed Decision Fusion for Large Scale IoT- Ecosystem

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