A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei, Marzieh; Khandagh, Saeed; Jafari Navimipour, Nima

doi:10.3390/s23167233

Cited by 14 publications

(2 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimization techniques further enhance the costeffectiveness of sensor deployments. Leveraging optimization algorithms, such as genetic algorithms or particle swarm optimization, can aid in determining the optimal locations for sensor placement [49,50]. This strategic positioning ensures maximum coverage and accuracy while minimizing the number of sensors required.…”

Section: B: Ph Sensormentioning

confidence: 99%

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

Rahu,

Chandio,

Aurangzeb

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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 Rsquared (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. INDEX TERMSData collection, Environmental monitoring, Internet of Things (IoT), Machine learning, Water quality analysis, Water quality class (WQC), Water quality index (WQI).

show abstract

Section: B: Ph Sensormentioning

confidence: 99%

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

Rahu,

Chandio,

Aurangzeb

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…IoT and cloud computing enable machine-to-machine connections through Wi-Fi-enabled medical devices, establishing the groundwork for a seamless data-sharing and analysis system [ [14] , [15] , [16] ]. Using IoT in smart healthcare, a patient's body parameters can be measured in real-time [ 17 ]. Personalized treatment, real-time data-driven decision-making, and remote patient monitoring are all made possible by IoT, which is currently revolutionizing the healthcare industry.…”

Section: Introductionmentioning

confidence: 99%

The applications of internet of things in smart healthcare sectors: a bibliometric and deep study

Ziwei,

Dongni,

Man

et al. 2024

Heliyon

View full text Add to dashboard Cite

TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments

Kheroua,

Doukha,

Moussaoui

2024

Proceedings of Third International Conference on Computing and Communication Networks

View full text Add to dashboard Cite

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Cited by 14 publications

References 96 publications

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

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

The applications of internet of things in smart healthcare sectors: a bibliometric and deep study

TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments

Contact Info

Product

Resources

About