Building Efficient IoT Systems with Edge Computing Integration

The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.