Image Transformation for IoT Time-Series Data: A Review

• URL: http://arxiv.org/abs/2311.12742v1
• Date: Tue, 21 Nov 2023 17:31:10 GMT
• Title: Image Transformation for IoT Time-Series Data: A Review
• Authors: Duygu Altunkaya, Feyza Yildirim Okay and Suat Ozdemir
• Abstract summary: Time-series data is high-dimensional and high-frequency. Deep learning algorithms have demonstrated superior performance results in time-series data classification. Recent studies show that transforming IoT data into images improves the performance of the learning model.
• Score: 1.7188280334580197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of the Internet of Things (IoT), where smartphones, built-in systems, wireless sensors, and nearly every smart device connect through local networks or the internet, billions of smart things communicate with each other and generate vast amounts of time-series data. As IoT time-series data is high-dimensional and high-frequency, time-series classification or regression has been a challenging issue in IoT. Recently, deep learning algorithms have demonstrated superior performance results in time-series data classification in many smart and intelligent IoT applications. However, it is hard to explore the hidden dynamic patterns and trends in time-series. Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies which use image transformation/encoding techniques in IoT domain. We examine the studies according to their encoding techniques, data types, and application areas. Lastly, we emphasize the challenges and future dimensions of image transformation.

Related papers

• DeepFeatIoT: Unifying Deep Learned, Randomized, and LLM Features for Enhanced IoT Time Series Sensor Data Classification in Smart Industries [2.2120045208641184]
  Internet of Things (IoT) sensors are ubiquitous technologies deployed across smart cities, industrial sites, and healthcare systems.<n>We propose a novel deep learning model, DeepFeatIoT, which integrates learned local and global features with non-learned randomized convolutional kernel-based features.<n>Our model's effectiveness is demonstrated through its consistent and generalized performance across multiple real-world IoT sensor datasets.
  arXiv  Detail & Related papers  (2025-08-13T03:47:33Z)
• Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder [0.0]
  This study focuses on investigating how the latent dimension impacts the performance of different deep learning classifiers when trained on latent vector representations of the train dataset. The encoder components are employed to project high-dimensional structured. CSV IoT botnet traffic datasets to various latent sizes.
  arXiv  Detail & Related papers  (2025-04-21T06:15:07Z)
• Leveraging Foundation Models for Zero-Shot IoT Sensing [5.319176383069102]
  Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices. ZSL aims to classify data of unseen classes with the help of semantic information. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing.
  arXiv  Detail & Related papers  (2024-07-29T11:16:48Z)
• Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification [4.487333825879047]
  Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Event fingerprints from deployed sensors have been used to detect spoofed events. We deploy advanced machine learning to detect event-spoofing assaults.
  arXiv  Detail & Related papers  (2024-07-29T02:52:59Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• Prototype of deployment of Federated Learning with IoT devices [0.0]
  A huge amount of the resource desired, data, is stored in mobile devices, sensors and other Internet of Things (IoT) devices. At the same time these devices do not have enough data or computational capacity to train good models. Federated Learning (FL) provides an innovative solution that allows devices to learn in a collaborative way.
  arXiv  Detail & Related papers  (2023-11-24T10:37:30Z)
• MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
  The next generation of machine learning systems must be adept at perceiving and interacting with the physical world. sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments. Existing efforts are often specialized to a single sensory modality or prediction task. This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
  arXiv  Detail & Related papers  (2023-11-10T18:13:08Z)
• IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective [10.350337750192997]
  We conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted.
  arXiv  Detail & Related papers  (2022-09-16T16:02:56Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review [0.0]
  A multitude of studies has been conducted with the intention of improving speed and accuracy. The optimal machine learning-based pattern recognition algorithms to be used with IoT devices are support vector machine, k-nearest neighbor, and random forest.
  arXiv  Detail & Related papers  (2022-01-10T00:54:51Z)
• Networked Time Series Prediction with Incomplete Data [59.45358694862176]
  We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. We conduct extensive experiments on three real-world datasets under different missing patterns and missing rates.
  arXiv  Detail & Related papers  (2021-10-05T18:20:42Z)
• Multi-Exit Vision Transformer for Dynamic Inference [88.17413955380262]
  We propose seven different architectures for early exit branches that can be used for dynamic inference in Vision Transformer backbones. We show that each one of our proposed architectures could prove useful in the trade-off between accuracy and speed.
  arXiv  Detail & Related papers  (2021-06-29T09:01:13Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.