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Research Proposal in Deep learning-driven Computation Offloading in Fog-Cloud Environment

   In a data-rich world, the proliferation of mobile devices becomes high, and resource-constrained mobile devices are incapable of performing high-computation processes due to resource limitations. An effective solution to overcome this issue is Computation offloading. Even though Cloud computing provides an appropriate solution to computation-offloading for mobile devices, increasing network delay issues occur often.
   Fog network provides resources and services outside the cloud environment at the edge of the network and closer to the end-users. However, identifying the optimal node for the computation offloading is crucial, and also near-end network approach is necessary for computational offloading. Hence, Deep-learning models accurately determine the location for computation offloading; that is, it identifies whether to offload in the neighbor node, fog node, or cloud node based on response time prediction. It improves the latency and energy efficiency while provisioning computational services to mobile devices.