Robust IoT time series classification with data compression and deep learning

Azar, Joseph; Makhoul, Abdallah; Couturier, Raphaël; Demerjian, Jacques

doi:10.1016/j.neucom.2020.02.097

Cited by 59 publications

(24 citation statements)

References 25 publications

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“…On the other hand, a IoT network differs from a WSN in terms of connectivity between each node, whereby the IoT node can be connected directly to the internet and has the ability to make decisions [ 46 , 47 ]. Therefore, a new way of aggregation and compression became in demand in IoT edge and sense nodes as the number of connected IoT devices and data increased exponentially during the last years [ 48 , 49 ].…”

Section: Internet Of Thingsmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

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Section: Internet Of Thingsmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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“…Several works have analyzed time series in IoT context. For instance, [7] studied the effects of lossy compression in IoT time series when applying deep-learning classification. They focused on proposing an efficient compression technique with an error-bound compressor for reaching a trade-off between compression and quality in univariate and multivariate time series.…”

Section: Related Work a Time Series Analysis In Iotmentioning

confidence: 99%

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

2020

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Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.

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“…Azar et al [16] studied the effects of using lossy data compression techniques on time series by using deep learning. Their main approach is the combination of error-bound compressor (Squeeze) and Discrete Wave Transform (DWT) lifting scheme obtaining a high data compression ratio.…”

Section: Related Workmentioning

confidence: 99%

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

Klus

Quezada-Gaibor

Torres-Sospedra

et al. 2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.

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Robust IoT time series classification with data compression and deep learning

Cited by 59 publications

References 25 publications

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

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