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Top1. Introduction
Due to the recent technological advancements in IoT, 5G, Big Data and Machine Learning, there will be more than 1.5 billion connected devices. The success of IoT based applications is characterized by reliability, availability, low latency, capacity and high data throughput. Numerous smart devices are associated with existing networks and empowered with a rich set of big data (Ray, 2018). Vehicle to vehicle (V2V) communication (Zorkany,Yasser,& Galal, 2018), smart homes, smart cities (Dubey et al., 2020), smart health (Rahaman et al., 2019), unmanned aerial vehicle (UAV) (Lagkas et al., 2018) are some of the imminent areas based on IoT Networks. These IoT applications generate massive data (Singh & Agrawal, 2018) and cloud computing provides a pathway for that data to travel to its destination. Adoption of the cloud computing is recognized as data processing and storage facility (Yan, 2017). All real time applications need just in time processing and quick action over the clouds. IoT applications may lead to the performance degradation of the computing nodes due to vast data transmission and the long distance between the data source and designated data-center (Tian, Wang, & Song, 2014; Sarkar, Chatterjee, & Misra, 2015; Peng et al., 2017; Luo et al., 2018; Al-Khafajiy et al., 2018). Now-a-days cloud computing facing many types of challenges like fault tolerance, reliability, availability, integrity etc. and key challenge is overloading (Rawshan, Islam, & Imran, 2016).
Fog Computing is a recent computational paradigm developed to enhance the power of cloud computing standards to run Geo-distributed, and other time sensitive applications throughout the network (Munir, 2018). The idea of fog computing is to keep computational and storage abilities at the edge of the network and can be achieved by locating servers and storage devices in close proximity to a client device. Fog nodes are expected to offer computing and transmission services with reliability, efficiency, and in a more secure fashion than clouds (Soo, Chang, and Srirama, 2016; Al-Khafajiy et al., 2018). Consider the proposed architecture of fog computing environment in Figure 1.
Figure 1.
Proposed Architecture of fog computing