Optimizing Edge Computing for IoT Ecosystems

Abstract

This research work focuses on the main aspects of edge computing in optimizing IoT ecosystems, including latency reduction, energy efficiency, and security improvement. By doing data processing at the edge of the network, edge computing enables such data processing. It boasts some advantages as compared to other cloud based architecture, especially when applied to this real time application for smart cities. The study also includes various optimization techniques such as secure communication protocols, energy efficient task scheduling and dynamic voltage and frequency scaling (DVFS). This displays how these various methods perform at synthesizing programs that improve system performance by using fewer resources. While performance benchmarks confirm performance gains of edge computing on the last properties, studies show that objective benchmarks indicate that edge computing is an enabler for the most crucial parts of smart city applications such as traffic control, environmental monitoring and healthcare. This study demonstrates how edge computing can cope with the increasing requirements for IoT networks and offer a scalable solution for data processing, energy consumption or data security in the smart cities.