Design and Evaluation of Energy-Efficient Edge Computing Architectures for IoT

Abstract

With the proliferation of Internet of Things (IoT) devices, the demand for efficient and low-latency data processing at the network's edge has surged. This paper presents the design and evaluation of energy-efficient edge computing architectures tailored for the specific requirements of IoT applications.

Our research explores the unique challenges associated with edge computing, including constrained resources and stringent power constraints. We introduce a novel architectural framework that combines lightweight processing elements, optimized communication protocols, and intelligent workload management to minimize energy consumption while ensuring the timely execution of IoT tasks.

Through a comprehensive evaluation, we demonstrate the effectiveness of our proposed architecture. We compare its performance against traditional cloud-based solutions, highlighting the significant reduction in latency and energy consumption achieved at the edge. Additionally, we assess the scalability and adaptability of our architecture across diverse IoT use cases.

This research contributes to the ongoing efforts to harness the full potential of IoT by enabling energy-efficient edge computing. Our findings have implications for various domains, including smart cities, industrial automation, and healthcare, where real-time data processing and energy conservation are paramount.