Resource-aware Deep Learning for Wireless Fingerprinting Localization

TL;DR

This paper discusses the development of resource-aware deep learning models for wireless fingerprinting localization to reduce energy consumption and carbon footprint, emphasizing sustainability in AI applications.

Contribution

It introduces a methodology for assessing DL model complexity and energy use, and demonstrates how to create more resource-efficient fingerprinting models.

Findings

Proposes a framework for evaluating model energy consumption and carbon footprint.

Shows how to develop more sustainable deep learning models for wireless localization.

Compares existing works based on complexity and environmental impact.

Abstract

Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, with the increasing complexity these methods become computationally very intensive and energy hungry, both for their training and subsequent operation. Considering only mobile users, estimated to exceed 7.4 billion by the end of 2025, and assuming that the networks serving these users will need to perform only one localization per user per hour on average, the machine learning models used for the calculation would need to perform $65 \times 10^{12}$ predictions per year. Add to this equation tens of billions of other connected devices and applications that rely heavily on more frequent location updates, and it becomes apparent that localization will contribute significantly to carbon emissions unless more energy-efficient models are developed and used. In this Chapter, we discuss the latest results and trends in wireless localization and look at paths towards achieving more sustainable AI. We then elaborate on a methodology for computing DL model complexity, energy consumption and carbon footprint and show on a concrete example how to develop a more resource-aware model for fingerprinting. We finally compare relevant works in terms of complexity and training CO$_2$ footprint.