Edge Computing: Enhancing IoT Performance through Decentralized Data Processing and Real-Time Decision-Making

Abstract

The growing proliferation of Internet of Things (IoT) devices has led to the generation of massive volumes of data, which are traditionally transferred to centralized cloud data centers for storage and processing. However, this approach often leads to network performance challenges, including increased latency, bandwidth consumption, and migration costs. Edge computing, a decentralized architectural strategy, has emerged as a solution to these issues by placing computation and storage resources closer to the end users and devices, minimizing data travel and optimizing network efficiency. This paper explores the role of edge computing in mitigating the limitations of cloud-based solutions, highlighting its benefits such as reduced latency, bandwidth optimization, enhanced privacy and security, real-time decision-making, scalability, and improved application performance. Key applications of edge computing in areas such as traffic control, content caching, healthcare, and autonomous driving are discussed, demonstrating its ability to support real-time, compute-intensive applications. Additionally, the architecture of edge computing, including cloudlets, multi-access edge computing (MEC), and the integration with 5G networks, is outlined as essential to enabling future advancements in IoT and real-time systems.