LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations

Author: B. Sliwa, N. Piatkowski, C. Wietfeld
Journal: ICC
Year: 2020

Citation information

B. Sliwa, N. Piatkowski, C. Wietfeld,
ICC,
2020,
https://doi.org/10.48550/arXiv.2001.10189

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, compilerspecific 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-inthe-loop approach explicitly considering the actual compilation
toolchain of the target IoT platform. LIMITS focuses on highlevel 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 learningenabled IoT systems.