Novel Approach for Improving Secure Scheduling in Fog Environment and in Context of Smart Homes

Authors

  • Ruchika Department of computer Science and Applications, M.D. University, Rohtak-124001, Haryana, India
  • Rajender Singh Chhilar Department of computer Science and Applications, M.D. University, Rohtak-124001, Haryana, India

Keywords:

Fog computing, Internet of Things, Smart Home, Scheduling, Performance, machine learning

Abstract

The increasing demand for Internet of Things-based smart home automation systems in the modern period is having an effect on the scheduling in fog environments. During the implementation of a smart home automation system, there is a pressing need for an innovative strategy that is able to significantly enhance the scheduling problems and fog environment. There are many different scheduling techniques available, each of which has its own set of limitations. The incoming real-time home automation is handled by a fog node that functions like a little cloud and has restricted resources. This node evaluates the input locally and sends a response back to the edge device. This method has the benefit of minimizing latency, which is widely recognized as one of the most significant limitations of cloud computing in the present day. Because fog computing is still in its early stages of development, it faces numerous obstacles and challenges, such as the limited resources of fog nodes and the processing of real-time job(s) while making the most use of available resources, which is also referred to as task scheduling in fog computing. Task scheduling is one of the main components of fog computing. It has the potential to improve the latency of a service and decrease the amount of network traffic. In this study, we analyze a variety of problems and difficulties associated with fog task scheduling using previous research. In addition, the articles provide a unique task scheduling model that is tailored specifically for use in fog computing environments in context of smart home, which offers a solution to a number of the challenges associated with task scheduling. Present research is providing task scheduling and allocation of optimized and authentic nodes to pending task by integrating machine learning mechanism with task scheduling.

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Published

16.01.2023

How to Cite

Ruchika, & Chhilar, R. S. . (2023). Novel Approach for Improving Secure Scheduling in Fog Environment and in Context of Smart Homes. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 23–29. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2473

Issue

Vol. 11 No. 1s (2023): Advances and Innovations in Deep Learning Applications

Section

Research Article

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