Optimization of Response Time in Software Defined IoT Networks Using Cloud-Fog Computing

Abstract :

The Internet of Things uses the cloud to process information received from electronic devices. Powerful servers located far from the sensors perform the processing. IoT devices send requests to the cloud and receive results from it. In some IoT applications, response time and latency are important. Therefore, latency should be reduced as much as possible. Sending information to the cloud itself entails latency. Therefore, the use of fog along with the cloud plays a fundamental role in the IoT. The use of fog and cloud computing in the field of IoT is a significant topic for researchers. To facilitate this process, software-defined networks have emerged as a vital component. These networks enable centralized control and management of the network. They also ensure optimal resource utilization and seamless connectivity by dynamically directing data flows to fog or cloud resources based on real-time conditions. Fog computing refers to the deployment of resources near the network sensor. By doing so, fog computing aims to reduce latency and bandwidth usage while improving overall system performance. It does this by utilizing local computing capabilities to process data. This research uses the proposed architecture for IoT networks and modeling different parts of this architecture using queuing theory to reduce response time using fog-cloud computing and obtain network quality of service parameters through mathematical analysis. In the following, the residual energy and latency comparison graphs for health and lighting applications as well as the use and non-use of fog-cloud computing are plotted. The graphs show that the use of fog-cloud computing reduces response time and the lighting application using fog has more residual energy and the health application has less latency. The simulation was performed using NS2 software in the smart home application.

References:

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