Edge Computing in IoT Networks: Enhancing Efficiency, Reducing Latency, and Improving Scalability.

Abstract Aoday, the Internet of Things (IoT) is changing fields by allowing interconnected devices to collect, share, and process data. As for traditional IoT networks that depend on centralized cloud computing, they come with high latency, redundant bandwidth consumption and energy inefficiency. This paper examines edge computing and identifies it as an enabling solution to these challenges. This facilitates real-time analytics of larger groups of data from smaller inputs and is the key characteristic of the edge computing model, by processing data closer to the source; edge computing minimizes latency, optimizes bandwidth usage, and enhances scalability. It examines architectural designs, optimization techniques, and practical applications of edge computing. The empirical evidence also shows that edge computing achieves up to 80% latency reduction, compared to the cloud, a bandwidth saving due to the fact that edge computing could process data at the source (thereby reducing data transfer to the cloud), and that edge computing could reduce overhead energy consumption by approximately 90% compared to cloud computing. The solutions proposed include hierarchical architectures, dynamic resource allocation, and integration with the blockchain, tackling challenges such as scalability, security, and energy efficiency. This work concludes that edge computing is a major breakthrough in iot networks and an enabling technology for real-time, efficient and sustainable applications.