Abstract
The rapid expansion of Internet of Things (IoT) ecosystems has placed enormous demands on cloud infrastructure, creating bottlenecks in data transmission, latency, and energy consumption. Edge computing has emerged as a promising paradigm to address these challenges by processing data closer to the source. However, most existing edge computing systems suffer from inefficiencies in energy utilization and resource allocation, limiting their effectiveness in real-time IoT applications. The aim of this research is to design and implement an energy-efficient edge computing architecture tailored for real-time IoT environments. The study seeks to reduce energy consumption while maintaining low latency, ensuring scalability, and providing robust quality-of-service (QoS) for diverse IoT workloads. The proposed method integrates a lightweight virtualization framework, dynamic task scheduling algorithms, and an adaptive power management scheme into a multi-tier edge architecture. Performance was evaluated using Raspberry Pi 4 edge nodes (1.5 GHz CPU, 4 GB RAM), connected to IoT sensors simulating smart home and industrial monitoring environments. Metrics such as latency (ms), throughput (Mbps), and energy consumption (Joules) were measured and compared with conventional cloud-based models. Results showed that the proposed architecture reduced average latency by 47% (from 120 ms to 64 ms), improved throughput by 35% (from 18.5 Mbps to 25 Mbps), and decreased energy consumption by 42% (from 12.5 J to 7.2 J per transaction). Figure 2 showed the latency reduction in edge vs. cloud model (ms). Figure 3 showed the throughput comparison of proposed architecture vs. baseline (Mbps). Figure 4 showed the energy consumption across task loads (Joules). These improvements demonstrate the architecture’s suitability for real-time IoT applications such as smart healthcare monitoring, autonomous vehicles, smart grids, and industrial automation. By optimizing energy efficiency without compromising performance, the proposed solution advances the sustainability and scalability of future IoT deployments.