An Efficient Multihop Edge Enabled Architecture for Time Constraint Application

Due to the increasing use of Internet of Things (IoT) devices and data-driven platforms, a huge volume of data has been transacted between the cloud and the end user. The automated tools such as Artificial Intelligence based platforms ChatGPT, Otter etc are proven to be building block for next generation data enabled systems. With the growing number of data-oriented applications, cloud computing is required to enhance the computational capacity and reduce the burden of handling concurrent requests. Cloud is providing all the support in terms of infrastructure demand, but there are challenges in terms of serving the time constraint request. Due to the restricted bandwidth, computational execution time, and resource availability when the applications and data are expanding in unavoidable situations. As the cloud is far from IoT devices to serve time-sensitive requests, multi hop communication is required to deploy to overcome this issue. Much research is done in Fog and Edge computing, but still it is required to resolve the problem of higher latency when pushing the compute intensive task to cloud layers. Edge computing brings the relevant resources near to the edge of the network. In this paper, the edge computing architecture has been designed to perform the multi hop communication and compared the result between the Cloud, Fog and Edge layers. The architecture is simulated using the iFogSim2 simulator and benchmark configurations are used. The results indicate that the latency and the usage of the network at the Edge layer is reduced compared to Fog and Cloud, which concludes that the designed architecture is suitable for the time constraint applications.