Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

TL;DR

This paper proposes a lightweight hybrid CNN-ELM model for multi-building and multi-floor classification in indoor positioning, achieving faster performance with minimal accuracy loss, suitable for resource-constrained devices.

Contribution

A novel lightweight CNN-ELM hybrid model that enhances classification speed while maintaining high accuracy for indoor positioning tasks.

Findings

Model is 58% faster than benchmarks.

Achieves near state-of-the-art accuracy with less computational resources.

Suitable for power-constrained indoor positioning devices.

Abstract

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58\% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1\%