Fully simulated and model based power consumption estimation of internet of things devices

Abstract

The rapid proliferation of Internet of Things (IoT) devices, driven by the demand for efficient computing units integrated into various networks, underscores the critical need for energy efficiency. As these devices often operate in mobile settings, optimizing energy consumption becomes paramount to minimize maintenance costs associated with battery replacement or recharge. Additionally, power efficiency directly impacts the portability of IoT devices, enhancing their usability and effectiveness in diverse environments. Software and hardware factors are influential on the energy consumption of IoT devices. Due challenges of battery replacement, IoT devices rely heavily on software-based power management for optimization. Thus, software updates playing a significant role in battery life expectancy. To plan maintenance processes effectively, manufacturers and service providers must accurately estimate energy consumption and battery lifetime, necessitating a holistic approach to power estimation. Traditional methods of energy consumption estimation, reliant on physical measurements, are impractical due to the extensive hardware and software design iterations required. Consequently, a fully simulated, model-based approach to power consumption estimation emerges as essential, especially considering the frequent update requirements of IoT devices. Such an approach enables accurate estimation throughout design changes and updates, facilitating efficient planning and management of power consumption across various scenarios. This thesis proposes a comprehensive energy consumption model tailored for IoT devices, complemented by fully simulated, model-based system-level power estimation approaches. By leveraging simulation environments like Open Virtual Platform (OVP), the proposed methodology achieves approximately \%97 accuracy in typical real-life scenarios. Notably, the methodology eliminates the need for completed hardware and software designs, enabling efficient power estimation throughout the development and operational phases of IoT devices. In conclusion, the study contributes a novel methodology for accurate power consumption estimation in IoT devices, addressing the challenges posed by evolving hardware and software requirements. By embracing simulation-based modeling and system-level approaches, the proposed methodology offers a practical and efficient solution for managing power consumption in IoT devices, ultimately enhancing their usability, reliability, and sustainability in diverse application domains. All these advantages have been validated through different applications, and the results have been shared within the scope of the thesis study.