Analyze Combining Iot with Edge Computing to Improve Storage and Transmission

Abstract

The ubiquitous nature of the Internet of Things (IoT) has made it possible to gather and analyze vast amounts of data, which has profound implications for our ability to make sound decisions. Critical smart-world infrastructures are monitored and controlled by millions of sensors and devices that are constantly producing data and exchanging essential information via sophisticated networks. Edge computing has evolved as a new paradigm to address the IoT and localized computing needs as a way to prevent the rise in resource congestion. Edge computing, in contrast to the more widely recognized cloud computing, will move data computation or storage closer to the network's ''edge,'' where it will be closer to the end users. As a result, the latency of message exchange can be greatly reduced by dispersing computing nodes around the network rather than relying solely on a centralized data center. In addition, the decentralized architecture can smooth out traffic spikes on Internet of Things networks, shorten response times for real-time IoT applications, and lower transmission latency between edge/cloudlet servers and end users. Additionally, the system can increase the lifetime of individual nodes by shifting the burden of computation and communication from nodes with limited battery supply to nodes with high power resources. In this research, we analyze the possibility of combining IoT with edge computing and the effect of edge computing on the efficiency of storage and transmission in IoT network. We group edge computing into its architectural components and compare their relative effectiveness in terms of latency, bandwidth utilization, power consumption, and other parameters. Taking into account edge computing's privacy, authenticity, and resilience concerns, we also provide a paradigm for assessing IoT network security using edge computing. We conclude by comparing and contrasting edge computing with traditional cloud computing architectures for various IoT use cases.