Convergence Perceptual Model for Computing Time Series Data on Fog Environment

The evolution of fog/edge paradigm is at the rising edge that complements data analysis and data computing algorithms in a big data platform, The role of fog nodes in a fog computing set up shall best suit time sensitive applications especially on virtual clusters supporting edge devices in sensing, processing, controlling and action planning, there by replacing the non-virtualized complicated computing mechanisms resolving the existing complexity in cloud computing storage and retrieval algorithms. There is an emerging need for addressing collaborative processes in various sectors not limiting to computing digital data on big data platform but also the futuristic industrial revolution to replace the existing techniques and technologies in industrial automation. The proposed work reveals a novel framework that supports the cutting edge of fog node fitting as an intermediate layer between the edge devices and cloud storage. Computational by means of fog-node attracts the need by replacing existing cloudlets time complexity in resource management. Hence fog as things operates as a controller in cyber physical systems like controlling transmission lines of high volt system connections, monitoring smart metering that has a wide range of applications through the integration of the industrial automation process.