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An Efficient Mobile Edge Computing based Resource Allocation using Optimal Double Weighted Support Vector Transfer Regression

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Abstract

Mobile edge computing (MEC) technology is gaining more attention in smart cities due to its powerful computation capability. However, there arise complications related to security and privacy while transmitting and processing raw data to other cloud or MEC servers. This makes the users unwilling to update their private information on the cloud servers. To tackle this issue, we proposed a novel approach for optimal task scheduling and resource allocation processes in this paper. The proposed ‘double-weighted support vector transfer regression based flow direction (DSTR-FD) approach’ resolves the issues of resource management of edge servers and makes optimal task offloading decisions with minimized energy consumption. Here, the model parameters such as weight functions, regularization parameters, and kernel parameters of the DSTR network are tuned using the flow direction (FD) algorithm. The proposed method thus provides better data privacy without sharing the original data with other servers along with minimizing the utilization of energy in the Internet of Things (IoT). The efficiency of the proposed DSTR-FD approach is evaluated by comparing its results with other states of art methods. The simulation experiments illustrate that the proposed DSTR-FD approach effectively minimizes the energy utilization of all IoT devices.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Madhav Institute of Technology and Science (MITS), Gwalior, Madhya Pradesh, 474005, India

    Kuldeep Narayan Tripathi, Gagandeep Kaur & Rohit Agrawal

  2. Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India

    Nitin Arora

Authors
  1. Kuldeep Narayan Tripathi
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  2. Gagandeep Kaur
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  3. Nitin Arora
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  4. Rohit Agrawal
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Contributions

All authors agreed on the content of the study. KNT,GK, NA and RA collected all the data for analysis. KNT agreed on the methodology. KNT,GK, NA and RA completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to Kuldeep Narayan Tripathi.

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Tripathi, K.N., Kaur, G., Arora, N. et al. An Efficient Mobile Edge Computing based Resource Allocation using Optimal Double Weighted Support Vector Transfer Regression. J Grid Computing 21, 49 (2023). https://doi.org/10.1007/s10723-023-09680-z

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