# Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services

arXiv: https://arxiv.org/abs/2304.06414 · HTML: https://mlsanity.com/paper/2304.06414/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2023.

## Built on by (most cited first, 3 of 3)

- [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024)
- [The Rise and Fall(?) of Software Engineering](https://mlsanity.com/paper/2406.10141/) (2024)
- [Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case](https://mlsanity.com/paper/2603.12001/) (2026)

## Abstract (arXiv)

Energy consumption and carbon emissions are expected to be crucial factors for Internet of Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while Artificial Intelligence (AI) further penetrates the "edge" in order to satisfy the need for highly-responsive and intelligent services. To date, several edge/fog emulators are catering for IoT testing by supporting the deployment and execution of AI-driven IoT services in consolidated test environments. These tools enable the configuration of infrastructures so that they closely resemble edge devices and IoT networks. However, energy consumption and carbon emissions estimations during the testing of AI services are still missing from the current state of IoT testing suites. This study highlights important questions that developers of AI-driven IoT services are in need of answers, along with a set of observations and challenges, aiming to help researchers designing IoT testing and benchmarking suites to cater to user needs.