Dynamic Clock Reconfiguration for the Constrained IoT and its Application to Energy-efficient Networking

TL;DR

This paper introduces ScaleClock, a system for dynamic clock reconfiguration in IoT devices that optimizes energy efficiency and performance by abstracting hardware clock complexities and enabling autonomous adjustments.

Contribution

It presents a novel runtime subsystem for dynamic clock reconfiguration on constrained IoT devices, implemented on RIOT OS, with a platform-independent DVFS mechanism.

Findings

Reduced MCU energy consumption by 40% in network communication tasks

Demonstrated effective dynamic clock scaling on real IoT devices

Enabled autonomous hardware performance adaptation

Abstract

Clock configuration takes a key role in tuning constrained general-purpose microcontrollers for performance, timing accuracy, and energy efficiency. Configuring the underlying clock tree, however, involves a large parameter space with complex dependencies and dynamic constraints. We argue for clock configuration as a generic operating system module that bridges the gap between highly configurable but complex embedded hardware and easy application development. In this paper, we propose a method and a runtime subsystem for dynamic clock reconfiguration on constrained Internet of Things (IoT) devices named ScaleClock. ScaleClock derives measures to dynamically optimize clock configurations by abstracting the hardware-specific clock trees. The ScaleClock system service grants portable access to the optimization potential of dynamic clock scaling for applications. We implement the approach on the popular IoT operating system RIOT for two target platforms of different manufacturers and evaluate its performance in static and dynamic scenarios on real devices. We demonstrate the potential of ScaleClock by designing a platform-independent dynamic voltage and frequency scaling (DVFS) mechanism that enables RIOT to autonomously adapt the hardware performance to requirements of the software currently executed. In a use case study, we manage to boost energy efficiency of constrained network communication by reducing the MCU consumption by 40 % at negligible performance impact.