# Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies

arXiv: https://arxiv.org/abs/2104.00972 · HTML: https://mlsanity.com/paper/2104.00972/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT](https://mlsanity.com/paper/1812.04872/) (2018)
- [Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection](https://mlsanity.com/paper/1701.01077/) (2017)
- [Learning to Detect Anomalous Wireless Links in IoT Networks](https://mlsanity.com/paper/2008.05232/) (2020)

## Built on by (most cited first, 8 of 8)

- [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024)
- [Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS](https://mlsanity.com/paper/2204.02802/) (2022)
- [On Designing Data Models for Energy Feature Stores](https://mlsanity.com/paper/2205.04267/) (2022)
- [Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach](https://mlsanity.com/paper/2310.08088/) (2023)
- [Image Transformation for IoT Time-Series Data: A Review](https://mlsanity.com/paper/2311.12742/) (2023)
- [A Representation Learning Approach to Feature Drift Detection in Wireless Networks](https://mlsanity.com/paper/2505.10325/) (2025)
- [Graph Neural Networks Based Anomalous RSSI Detection](https://mlsanity.com/paper/2505.15847/) (2025)
- [Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications](https://mlsanity.com/paper/2508.00042/) (2025)

## Abstract (arXiv)

The number of end devices that use the last mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and require reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection solutions are required. In this paper, we perform a first time analysis of image-based representation techniques for wireless anomaly detection using recurrence plots and Gramian angular fields and propose a new deep learning architecture enabling accurate anomaly detection. We elaborate on the design considerations for developing a resource aware architecture and propose a new model using time-series to image transformation using recurrence plots. We show that the proposed model a) outperforms the one based on Grammian angular fields by up to 14 percentage points, b) outperforms classical ML models using dynamic time warping by up to 24 percentage points, c) outperforms or performs on par with mainstream architectures such as AlexNet and VGG11 while having <10 times their weights and up to ≈8% of their computational complexity and d) outperforms the state of the art in the respective application area by up to 55 percentage points. Finally, we also explain on randomly chosen examples how the classifier takes decisions.