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Internet of Things Data Analytics (IoTDA)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 17551

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Dr. Eyhab Al-Masri

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Department of Computer Science and Systems, School of Engineering and Technology (SET), University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA 98402, USA
Interests: services computing; web of things; edge computing; distributed sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chi-Hua Chen

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Guest Editor

College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
Interests: healthcare systems; augmented reality; big data; deep learning; internet of things; data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Alireza Souri

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Guest Editor

Department of Computer Engineering, Islamic Azad University Tehran Science and Research Branch, Tehran 14665-678, Iran
Interests: Internet of things; data mining; cloud computing, formal verification

Dr. Olivera Kotevska

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Guest Editor

Computer Science and Mathematics, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: computational data analytics; Internet of things

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is not only about connecting billions of devices to the Internet, but also about connecting people, services, applications, businesses, and infrastructures, among many others. What makes the IoT even more interesting is how data and technology can be blended to build sustainable IoT Data Analytics (IoTDA) applications. With billions of devices embedded in environments, buildings, vehicles, or products and continuously generating huge amounts of real-time data in many different formats, building sustainable IoTDA applications is becoming more challenging. One of the main hurdles is determining a suitable environment for processing IoT data. While it is envisioned that IoT applications would typically perform data processing on the cloud, a growing number of limitations in meeting applications’ demands is prompting researchers to investigate more efficient ways for processing data near IoT devices, particularly for applications that require a low-latency response. With the growing number of IoT devices that have very limited computational abilities, the emergence of micro-clouds (or fog nodes) near data sources to create sustainable IoTDA applications makes this very timely. The IoTDA 2020 workshop provides researchers and practitioners a venue to discuss possible new methods for building sustainable IoTDA applications and develop methods and techniques to investigate existing IoTDA limitations. In this context, the workshop’s ambition is to help in shaping a community of interest on the existing research opportunities and challenges resulting from performing data analytics for IoT applications. In addition, the workshop helps in bringing researchers and practitioners together to investigate innovative ideas or approaches to this new research challenge with the main focus on developing sustainable IoTDA applications, fostering collaborations, and exchanging points of view. This Special Issue in Sensors is planned in conjunction with IoTDA 2020 (https://sites.google.com/view/iotda) and will include peer-reviewed feature papers presented at IoTDA 2020. Topics include, but are not limited to, the following:

Distributed intelligence for IoT data analytics;
Heterogeneous IoT data analytics for fog computing;
Data quality metrics for IoT applications;
Distributed data analytics in fog computing;
Big data IoT applications (e.g., smart city, manufacturing, e-health);
Visual analytics algorithms for IoT applications;
Middleware for IoT applications;
Mobility and context-awareness for IoT applications;
Process modeling for IoT applications;
Storage, querying, and diffusion of IoT data;
Data compression for constrained IoT Devices;
QoS guarantee for IoT applications;
Privacy, security, and trust issues in IoT applications;
Recovery schemes for IoT applications;
Internet of Things as a service (IoTaaS);
IoT data centers' data analytics;
IoT management capabilities for data centers

Dr. Eyhab Al-Masri
Prof. Dr. Chi-Hua Chen
Dr. Alireza Souri
Dr. Olivera Kotevska
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Research

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30 pages, 8535 KiB

Open AccessArticle

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

by Sayed Khushal Shah, Zeenat Tariq, Jeehwan Lee and Yugyung Lee

Sensors 2021, 21(18), 6023; https://doi.org/10.3390/s21186023 - 08 Sep 2021

Cited by 4 | Viewed by 3316

Abstract

Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown. Full article

(This article belongs to the Special Issue Internet of Things Data Analytics (IoTDA))

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25 pages, 1502 KiB

Open AccessArticle

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

by Gabriel Signoretti, Marianne Silva, Pedro Andrade, Ivanovitch Silva, Emiliano Sisinni and Paolo Ferrari

Sensors 2021, 21(12), 4153; https://doi.org/10.3390/s21124153 - 17 Jun 2021

Cited by 36 | Viewed by 5848

Abstract

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases. Full article

(This article belongs to the Special Issue Internet of Things Data Analytics (IoTDA))

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Review

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21 pages, 645 KiB

Open AccessReview

Data Quality Management in the Internet of Things

by Lina Zhang, Dongwon Jeong and Sukhoon Lee

Sensors 2021, 21(17), 5834; https://doi.org/10.3390/s21175834 - 30 Aug 2021

Cited by 21 | Viewed by 5961

Abstract

Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique. Full article

(This article belongs to the Special Issue Internet of Things Data Analytics (IoTDA))

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