Lossy Data Compression for IoT Sensors: A Review

Correa, Juan David Arias; Pinto, Alex Sandro Roschildt; Montez, Carlos

doi:10.1016/j.iot.2022.100516

Cited by 20 publications

(3 citation statements)

References 29 publications

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“…With the throughput of data communication networks and microprocessor speeds increasing exponentially over the past decades, it has become attractive to move certain functions to the network edge [255][256][257][258][259][260][261][262][263][264][265][266][267][268][269][270][271][272][273][274]. In decades past, early CPS were constrained [255] by the state of the art of microprocessor technology and network bandwidth, resulting in low-frequency, low-resolution data sampling from the sensors of the CPS.…”

Section: Anomaly Detection At the Network Edgementioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

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Section: Anomaly Detection At the Network Edgementioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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“…Rani, Khurana, Sharma, and Moudgil [6] discussed the different storage optimization techniques for IoT data [6]. Moreover, according to Correa, Pinto, and Montez [7], lossy Data Compression (DC) techniques can be better alternatives to the lossless ones as they are computationally less complex as well as provide a better compression ratio. In their paper, they discussed a category of lossy compression techniques which was based on the machine learning models using artificial neural network (ANN) architectures.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

Mahfuz

2023

Proceedings of the Canadian Conference on Artificial Intelligence

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In this data age, connected devices are continuously generating petabytes of images, text, and internet of things (IoT) sensor data. One approach to efficiently store this massive data is to extract the relevant and representative features and store only those features instead of the continuous streaming data. However, it raises a question as to the amount of information content we can retain from the data and if we can reconstruct the pseudo-original data when needed. By facilitating relevant and representative feature extraction, storage and reconstruction of near original pattern, we aim to address some of the challenges faced by the explosion of the streaming data. We present a preliminary study, where we explored multiple autoencoders for the concise feature extraction and reconstruction for human activity recognition (HAR) sensor data. Our Multi-Layer Perceptron (MLP) deep autoencoder achieved a storage reduction of 90.18%, where as convolutional autoencoder achieved 11.18%. For Long-Short Term Memory (LSTM) autoencoder the reduction was 91.47% and for convolutional LSTM autoencoder it was 72.35%. The storage reduction depended on the size and dimension of the concise representation. For higher dimensions of the representation, the storage reduction was low. But relevant information retention was high which was validated by the classification performed on the reconstructed data.

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“…In the case of lossy compression, some data are removed during the compression process, making it impossible to recover the original data. (12) This technique can be used to compress image, video, and audio files (e.g., JPEG, MPEG-4, and MP3 files, respectively). A large set of compression algorithms is used or directed to a specific domain.…”

Section: Introductionmentioning

confidence: 99%

Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

El-Hageen¹,

Albalawi²,

Alatwi³

et al. 2022

Sensors and Materials

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Time series data are collected through most of the applications that permeate our lives today. Internet of Things (IoT) sensor data are generated through smart applications and stored in databases. Time series databases require huge storage spaces, as over time they consume a large amount of memory. In this paper, we propose an enhanced compression algorithm for time series data generated by IoT systems that monitor the production of electrical energy by solar panels. The best way to ensure that solar energy systems have high efficiency is to continuously monitor all electrical and environmental factors. However, this requires the collection of enormous quantities of data that can be used to detect defects in the generation of electric energy or in solar panels. As the data must be available for analysis, a lossless compression algorithm is needed. In addition, the compressed data must be in a format that can be queried to perform analysis operations dependent on speed; this means that the decompression of data should not be timeconsuming. Our results showed the high speed of the compression process along with good compression rate (16.6%) after applying the proposed compression algorithm.

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Lossy Data Compression for IoT Sensors: A Review

Cited by 20 publications

References 29 publications

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

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