Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

With the increasing level of IoT applications, computation offloading is now undoubtedly vital because of the IoT devices limitation of processing capability and energy. Computation offloading involves moving data from IoT devices to another processing layer with higher processing capability. However, the size of data offloaded is directly proportional to the delay incurred by the offloading. Therefore, introducing data reduction technique to reduce the offloadable data minimizes delay resulting from the offloading method. In this paper, two main strategies are proposed to address the enormous data volume that result to computation offloading delay. First, IoT Canonical Polyadic Decomposition for Deep Learning Algorithm is proposed. The main purpose of this strategy is to downsize the IoT offloadable data. In the study, the Kaggle-cat-and-dog dataset was used to evaluate the impact of the proposed data compression. The proposed method downsizes the data significantly and can reduce the delay due to network traffic. Secondly, Rank Accuracy Estimation Model is proposed for determining the Rank-1 value. The result of the proposed method proves that the proposed methods are better in terms of data compression compared to distributed deep learning layers. This method can be applied in smart city, vehicular networks, and telemedicine etc.