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Table 1 Related work summary

From: An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Reference Evaluation metrics Pros Cons
[20] MAE, RMSE, IA Feasibility and practicality were verified experimentally for forecasting PM2.5 using their proposal Algorithmic predictions did not follow real trend accurately and were a bit shifted and disordered
[22] MAE, RMSE CNN-GRU and CNN-LSTM worked better for PM10 and PM2.5, respectively Hybrid models weakly predicted future highest and lowest levels of PM2.5
[23] RMSE, R2 After using multiple algorithms, it was found that Extra Trees gives the best performance The study was limited in the number of machine learning algorithms compared. There was a bit of a shift between actual and predicted values for most algorithms
[24] RMSE, MAE CNN could extract air quality features, shortening training time, whereas LSTM could perform prediction using long-term historical input data More evaluation parameters, stating closeness to real values like R2 or IA rather than only errors metrics, could have been used to confirm their models' performance
[25] NMSE, FB and FA2 PM2.5 concentration was predicted using meteorological parameters and PM10 and CO without a history of PM2.5 itself More machine learning models could have been used to test their methodology further
[26] SMAPE The ensemble of the three models (AccuAir) proved to be better than the individual components tested They did not use LSTM in their Seq2Seq model, although it was proven to be very efficient in time series prediction
[27] RMSE, MAE and MAPE Their model was compared to Multilayer Perceptron (MLP) and LSTM models and proved to be more stable and accurate Their system predicts only the daily average and cannot be deployed to predict the hourly or real-time concentration of PM2.5
[28] A comparison of prediction values vs. real value using different sample sets Their proposed system uses many sensors to ensure accuracy and minimize monitoring cost. The system is scalable and suitable for big data analysis The study did not use any clear evaluation metric; instead, they presented a comparison of prediction values vs. actual value using different sample sets
[29] Calculating AQI and comparing two setups with and without measurements flattened and calibration and accumulation algorithms employed They developed a system that saves bandwidth and energy consumption Further processing by the edge can save even more bandwidth and energy consumption. However, no prediction exists on the edge devices or the cloud side
[30] There is no evaluation metric of their system, only a proof of concept It tackles security issues of that kind of IoT system. Their IoT solution is scalable, reliable, secure and has HA (high availability) The system is used primarily for monitoring rather than conducting prediction of future pollution levels. It relies on central management and central prediction rather than performing prediction on edge devices
[31] RMSE, MAE and F1 It comprised both prediction and classification to make an alarm system. LSTM was compared to SVR as a baseline, and LSTM was proven to be a better algorithm Their research did not include a comparison to other works and used only one base model