IoT-Edge Communication Protocol based on Low Latency for effective Data Flow and Distributed Neural Network in a Big Data Environment

The sky-scraping increase in computer power includes in-depth study for all. In-depth learning provides accurate information at all times compared to other learning algorithms. Then, the Internet of Things (IoT) has grown in popularity in the field, for example, smart cities, exploration of oil, communications, etc. Edge / Fog IT support solve major challenges faced by the Internet of Things such as internet bandwidth, latency, and fixed network connection. Edge computing is spreading in a virtual environment, which requires a lot of time for machine learning. This paper aims to integrate the flow of data and disseminate the IoT Edge environment to reduce exploration and increase reliability from the data generation point of view. Based on this, a Troubleshooting of the Distributed Neural Network Model (DF-DDNN) was developed from the model of IoT Edge for the largest environment Data. Our suggested technique reduces latency by almost 33% compared to the long-developed cloud model of the Internet of Things.