Edge Computing for IoT Networks: Reducing Latency through Intelligent Data Offloading

Abstract

The fast-growing number of Internet of Things (IoT) devices have contributed to the rapid growth of data generated, which has become a breadth to centralized cloud infrastructures. Conventional cloud-based systems tend to have high latency, bandwidth overload and low real-time response, all of which are severe limitations to latency-sensitive systems like autonomous vehicles, smart healthcare, and industrial automation. Edge computing has been identified as a prospective paradigm that takes computation and storage services nearer to the data sources. In this paper, intelligent data offloading plans in an edge computing setup are discussed to shorten the latency in IoT networks. An adaptive offloading system according to workload description and network characteristics is suggested which allows allocating tasks optimally between the edge node and the cloud. Experimental results reveal that intelligent data offloading will greatly decrease the end-to-end latency and enhance the efficiency of the network in comparison to the traditional cloud-only strategies.Some of the issues though, include practical limitations such as resource constraints at edge nodes, security vulnerability and large-scale deployment. Future research directions consist of incorporating the concept of AI driven predicted offloading, federated learning in making distributed intelligence and adaptive security frameworks to make edge-IoT systems more scalable and resilient.