Alyani Ismail

Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Data are available on request from the authors. References 1, , and , “An Efficient Algorithm For Dynamic Task Offloading Using Cloudlets In Mobile Cloud Computing,” International Journal of Communication Systems 33, no. 13 (2020): e3890, https://doi.org/10.1002/dac.3890.

3. Results and Discussion In this section, the FWI published by the Malaysian meteorological department and 80% of the data measured by the IoT system in Section 2.1 are imported into the two neural network structures proposed in Section 2.2 for training (i.e., 37,844 samples). Then, the remaining 20% (i.e., 9462 samples) dataset is used to test the performance of the two structural training models.

3.2. Results of The Proposed Model The Canadian FWI system calculates the DMC (refer to Algorithm 1). The measured values of the IoT system and the weather parameters (i.e., temperature, humidity, and rainfall) published by METMalaysia are calculated to compare DMC with the actual DMC published by METMalaysia, as shown in Figure 12. Figure 12 shows the comparison of DMC calculation based on the Canadian model using the Internet of Things system and METMalaysia data.