LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations

01/28/2020
by   Benjamin Sliwa, et al.
0

Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advancements for IoT-based data analytics, there remains a huge methodological gap for the deployment phase of trained machine learning models. For given resource-constrained platforms such as Microcontroller Units (MCUs), model choice and parametrization are typically performed based on heuristics or analytical models. However, these approaches are only able to provide rough estimates of the required system resources as they do not consider the interplay of hardware, compiler specific optimizations, and code dependencies. In this paper, we present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. The solid foundations of validated low-level model implementations are provided by the coupled well-established data analysis framework Waikato Environment for Knowledge Analysis (WEKA). We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification, where we compare different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB and demonstrate its potential to catalyze the development of machine learning enabled IoT systems.

READ FULL TEXT

page 1

page 3

page 6

research
05/05/2020

Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promisin...
research
09/22/2020

From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)

In this paper, we illustrate how to enhance an existing state-of-the-art...
research
05/29/2022

Machine Learning for Microcontroller-Class Hardware – A Review

The advancements in machine learning opened a new opportunity to bring i...
research
02/12/2021

Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services

This doctoral dissertation proposes a novel approach to enhance the deve...
research
06/10/2021

IoT Virtualization with ML-based Information Extraction

For IoT to reach its full potential, the sharing and reuse of informatio...
research
12/12/2020

PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data

An automated machine learning framework for geospatial data named PAIRS ...
research
03/18/2020

Patient-centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts n...

Please sign up or login with your details

Forgot password? Click here to reset