A FoG Server-Driven Approach for Real-Time Data Analytics in Smart Application Environments
Deepika Sandhu, Anurag Kumar Tiwari, Sravan Kumar G, Gopari Gouthami, Chetan Sinhgadiya, Praveen Chouksey
Abstract
In this paper, we propose a new framework for real-time data analytics deployment in smart application environments through a fog server-driven approach. Critical challenges involving latency, bandwidth barriers, and scalability with existing traditional cloud solutions are addressed by the framework. Using the proposed architecture, fog servers are strategically placed at the edge of the network to process and analyze IoT-generated data with minimized response time and optimum resource utilization. Dynamic resource allocation, workload forecasting, and intelligent placement of services are used by the system to maintain high availability and performance. The effectiveness of this approach is validated with real-world datasets for both public and private IoT applications, showing superior results in response time, energy efficiency, and data privacy than state-of-the-art centralized models. The proposed methodology has several advantages for several smart applications such as smart cities, health care, developed automation, and environmental examining. The results showcase the great potential of fog computing in designing robust and efficient smart environments to broaden the scope of the next-generation IoT ecosystems.