Optimizing Edge Computing for IoT Applications: A Lightweight Framework for Real-Time Data Processing

Research Article

Abstract

The increasing deployment of Internet of Things (IoT) devices has led to a surge in real-time data generation, challenging traditional cloud-centric processing models due to latency, bandwidth, and privacy constraints. Edge computing addresses these limitations by bringing computation closer to data sources. However, the constrained resources at edge nodes demand lightweight and efficient processing frameworks. This paper proposes a modular, lightweight edge computing framework optimized for real-time IoT data processing. The framework comprises data acquisition, an adaptive scheduling engine, a real-time processing core, and a communication interface designed for minimal resource consumption. Performance evaluations conducted in a simulated smart agriculture environment demonstrate significant improvements in latency, throughput, and energy efficiency over cloud-only architectures. The results indicate that the proposed approach is scalable, adaptable, and suitable for latency-critical IoT applications.