Towards a Modular Digital Twin Framework for Energy-Optimized IoT Sensor Devices

Abstract

The Internet of Things (IoT) has transformed various industries by enabling seamless connectivity and data exchange between physical devices. However, optimizing the energy consumption of IoT platforms remains a significant challenge due to their complex and heterogeneous nature. In this paper, we propose a modular digital twin framework tailored for IoT platforms, aimed at evaluating data collection policies to optimize energy consumption and prolong device operational life. Our framework integrates hardware abstraction, simulation/emulation, update recommendations, and real-world synchronization to create a functional replica of IoT devices in a digital environment. We categorize IoT device modules into sensing units, communication units, and processing units, each with distinct operational profiles. To demonstrate the efficacy of our framework, we present a greenhouse monitoring IoT application, where temperature and humidity data are collected and transmitted using Zigbee protocol. Through a theoretical exposition, we illustrate the utility of our framework. Our approach lays the foundation for further research in optimizing IoT platforms for energy efficiency, fostering longer deployments, and promoting sustainability in IoT applications.