Assessing the Carbon Footprint of IoT-Enabled Deep Learning with IoTCO2

Core Concepts

IoTCO2 provides precise carbon footprint estimation for IoT-enabled DL, covering operational and embodied aspects.

Abstract

IoTCO2 introduces a modeling tool for accurate carbon footprint assessment in IoT-enabled DL. It addresses gaps in existing tools by considering both operational and embodied carbon footprints. The tool demonstrates a maximum deviation of ±21% compared to actual measurements across various DL models. Practical applications are showcased through user case studies.

Stats

Existing research falls short in estimating operational or embodied carbon footprint accurately for IoT-enabled DL. IoTCO2 shows a maximum ±21% deviation in carbon footprint values compared to actual measurements. Google reported values show that IoTCO2 estimates the embodied carbon footprint within 3.23% accuracy on average.

Quotes

"We introduce an end-to-end modeling tool, IoTCO2, tailored for precise carbon footprint estimation in IoT-enabled DL." "IoTCO2 demonstrates a maximum ±21% deviation in carbon footprint values compared to actual measurements across various DL models."

Key Insights Distilled From

IoTCO2

by Ahmad Faiz,S... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10984.pdf

Deeper Inquiries

How can IoTCO2's accuracy be improved further to reduce the ±21% deviation

To further improve the accuracy of IoTCO2 and reduce the ±21% deviation in carbon footprint estimations, several strategies can be implemented: Fine-tuning Predictive Models: Refining the Random Forests Regression (RFR) models used for operational energy prediction by incorporating more training data, optimizing hyperparameters, and enhancing feature selection techniques. Incorporating More Data Points: Increasing the diversity and quantity of data points for measuring energy consumption across different IoT devices, DL models, and hardware configurations to enhance the predictive capabilities of IoTCO2. Accounting for Environmental Factors: Considering external factors like ambient temperature variations or power source carbon intensity in calculations to provide a more comprehensive assessment of carbon footprints. Validation with Real-world Data: Conducting extensive validation studies with real-world measurements to validate predictions accurately and identify areas where IoTCO2 may need adjustments.

What are the potential implications of inaccurate carbon footprint assessments for IoT devices

Inaccurate carbon footprint assessments for IoT devices can have significant implications: Environmental Impact: Overestimating or underestimating carbon footprints can lead to incorrect decisions on sustainability measures, potentially resulting in higher environmental impact than anticipated. Resource Allocation: Misjudging carbon footprints may lead to misallocation of resources towards reducing emissions in areas that do not contribute significantly, diverting attention from critical areas needing mitigation efforts. Regulatory Compliance: Inaccurate assessments could result in non-compliance with environmental regulations or standards set by governing bodies, leading to legal repercussions or fines for organizations using IoT devices. Reputation Damage: Incorrectly reported carbon footprints can damage an organization's reputation among environmentally conscious consumers who prioritize sustainable practices.

How can the findings from IoTCO2 be applied to other areas beyond deep learning and IoT

The findings from IoTCO2 hold relevance beyond deep learning and IoT applications: Sustainable Practices Across Industries: The methodologies developed by IoTCO2 can be adapted to assess the environmental impact of various technologies beyond DL on diverse systems like cloud computing infrastructure, smart cities' networks, or autonomous vehicles. 2.Supply Chain Management: Organizations can utilize similar tools inspired by IoTCO2 principles to evaluate embodied emissions throughout their supply chains - aiding in making informed decisions about sourcing materials sustainably. 3Policy Making: Policymakers could leverage insights from IoTCO2 when formulating regulations related to technology deployment ensuring that emerging tech aligns with sustainability goals while minimizing adverse environmental effects. 4Consumer Awareness: By applying similar frameworks derived from IoTC02's approach into product labeling initiatives such as providing detailed information on products' lifecycle emissions will empower consumers making eco-conscious choices.