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An Optimal Novel Approach for Dynamic Energy-Efficient Task Offloading in Mobile Edge-Cloud Computing Networks

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Abstract

The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to resource limitations and security concerns inherent to these devices. In response, this paper introduces a Dynamic Energy-Efficient Offloading Algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption, and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and comparative analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user experience.

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Data Availability

The data that support the findings of this study include experimental results, processed data, and analytical scripts. These data are not publicly available due to privacy or ethical restrictions. However, detailed information about the methodology and results can be obtained from the corresponding author upon reasonable request.

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Mondal, A., Chatterjee, P.S. & Ray, N.K. An Optimal Novel Approach for Dynamic Energy-Efficient Task Offloading in Mobile Edge-Cloud Computing Networks. SN COMPUT. SCI. 5, 655 (2024). https://doi.org/10.1007/s42979-024-02992-1

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