Abstract:
Fog computing focus on local processing of tasks on edge Internet of Things (IoT) devices
known as fog devices. Nowadays IoT applications produce a large amount of data and require
powerful analytical approaches. These data should transmit to cloud data centres to extract useful information. In fog computing, these kinds of data handling by fog layer. The fog layer
consists of geo-allocated servers which are deployed on the network periphery. Each fog server
can be known as a lightweight version of the cloud server. In particular, it is preferred to the expand fog server, which reduces data amount before sending them to cloud data centres by using
TensorFlow, one of the most popular deep learning library, Google Cloud Platform and Deep
Neural Network. Deep neural networks can be known as a computer system modelled on the
human brain and neural system. In data classification, neural network provides fast and efficient
results. In this scenario fog server known as fog station and focused on implement analytical
applications in fog stations with the help of Docker and Kubernetes, except implementing them
in the main cloud server. Those applications belong to one or more fog devices. Through the
research work, studied and compared the effects of using TensorFlow, with multiple hidden
layers. TensorFlow library with the help of keras used to build the neural network model. The
experiment results of Rectified Linear Unit (ReLu), Leaky ReLU, Hyperbolic Tangent (tanH),
Exponential Linear Unit (eLu), sigmoid, softplus, softmax and softsign activation functions
have been evaluated. The experiment results demonstrated that the feasibility, efficiency and the
applicability of the proposed fog station. Finally, achieved (i) Centralized Management using
Fog Station, (ii) Dynamic Deployment using Docker, (iii) Efficient Management & Resource
Monitoring using Kubernetes, (iv) Real-Time Data Analytics using TensorFlow and GCP as
objectives.
