A Comparative Study of Various Machine Learning Algorithms in Fog Computing

Internet of Things is the reality of the future. It encompasses a vast prototype. The applications are limitless and the research potential is huge. Several unanswered questions still stand in the way. One of these challenging questions is the Network layer implementation of IoT. Cloud offers one perspective solution. Cloud services face the down side of connectivity and latency. Fog computing, as a complementing technology for Cloud answers the issues of latency and connectivity in the Cloud. Being novel idea in itself, Fog computing offers promising future growth. A major concept in Fog networks is its ability to learn and adopt to changing environment. This ability of the Fog devices to self-learn and improve itself is the core of machine-learning. Hence, machine-learning algorithms occupy central place in visualizing robust and effective Fog networks. This paper presents a comparative study of various machine-learning algorithms and their applications in the Fog network. The paper also projects the undiscovered research paradigms and future directions