Abstract
The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments.