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An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks

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Abstract

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flow prediction is based on the dataset generated from the simulations with NS-2.35 for multiple Wireless Sensor Networks (5- to -50 nodes). The performance comparison of different machine learning models with continuous data and discretized data is also presented. The proposed approach considerably reduces the execution time of the machine learning models for training purposes and also enhances the accuracy of prediction. The result analysis shows that the proposed approach is better compared to various machine learning methods. Also, the proposed approach is able to handle both continuous and discrete data. The datasets used in this work are available as a supplement at NDS and DDS link.

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Author information

Authors and Affiliations

  1. Research Scholar, Thapar Institute of Engineering and Technology, Patiala, India

    Jasminder Kaur Sandhu

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Jasminder Kaur Sandhu

  3. Computer Science and Engineering Department, Thapar University, Patiala, India

    Anil Kumar Verma & Prashant Singh Rana

Authors
  1. Jasminder Kaur Sandhu
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  3. Prashant Singh Rana
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Correspondence to Jasminder Kaur Sandhu.

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The authors declare no conflicts of interest. The article discusses about machine learning based discretization techniques for network analysis.

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Supplementary Information

Supplementary Information

The correlation values for NDS and DDS is shown in Table 5 : 

Table 5 Correlation for NDS and DDS
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The correlation for various dataset partitions is presented in Fig. 10:

Fig. 10
figure 10

Correlation

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The Coefficient of Determination values for NDS and DDS is shown in Table 6 : 

Table 6 Coefficient of Determination for NDS and DDS
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The coefficient of determination for various dataset partitions is presented in Fig. 11:

Fig. 11
figure 11

Coefficient of determination

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The Root Mean Square Error (RMSE) values for NDS and DDS is shown in Table 7 : 

Table 7 Root Mean Square Error for NDS and DDS
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The RMSE for various dataset partitions is presented in Fig. 12:

Fig. 12
figure 12

RMSE

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The accuracy values for NDS and DDS is shown in Table 8 : 

Table 8 The accuracy for NDS and DDS
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The accuracy for various dataset partitions is presented in Fig. 13:

Fig. 13
figure 13

Accuracy

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The time taken for NDS and DDS is shown in Table 9 : 

Table 9 The time taken for NDS and DDS
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The time taken for various dataset partitions is presented in Fig. 14:

Fig. 14
figure 14

Time taken

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Sandhu, J.K., Verma, A.K. & Rana, P.S. An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks. Wireless Pers Commun 112, 325–352 (2020). https://doi.org/10.1007/s11277-020-07028-4

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