Dianxin kexue (Apr 2024)
Computational measurement and task scheduling: a study on IoT edge device strategies
Abstract
The rapid advancement of mobile communications and artificial intelligence has catalyzed an exponential increase in intelligent devices and data generation. This surge necessitates enhance the computational resource capabilities, particularly in the Internet of things (IoT) environments, where there are pressing demand for improved resource management in terms of computation, latency, and energy efficiency. The concept of a computility network, which leverages interconnected computing nodes for resource sharing and optimization based on a unified measurement standard and task scheduling strategy, offers a promising solution for augmenting IoT systems' computational performance. However, the current models for computing resource measurement, predominantly focused on computational capacity, fall short in addressing the diverse and collaborative needs of various IoT devices. These devices often differ in network connectivity modes and exhibit sensitivity to power consumption. Moreover, prevalent task scheduling methods in computility network predominantly rely on centralized network routing nodes or management platforms. Such approaches are not well-suited for the unique characteristics of IoT devices, which are typically dispersed and constrained in resources. To address these challenges, a novel architecture for computing resource measurement tailored to IoT devices was introduced. A comprehensive and unified framework for measuring diverse aspects of computing resources in heterogeneous IoT environments was provided, including computation, storage, communication, power consumption, and power supply metrics. Building on this foundation, a distributed task scheduling strategy that intelligently aligned the disparate computing resources with specific business scenario requirements was proposed, thereby facilitating efficient resource management and task scheduling for IoT devices. To validate the effectiveness of the proposed architecture, it was applied to a smart home scenario. The empirical results demonstrate that the proposed architecture significantly enhances the sharing and scheduling of computing resources among IoT devices. It elevates the overall efficiency of IoT computing while concurrently reducing energy consumption, thereby offering a robust solution to the evolving demands of IoT systems.
