A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

DeMedeiros, Kyle; Hendawi, Abdeltawab; Álvarez, Marco

doi:10.3390/s23031352

Cited by 39 publications

(14 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary purpose of time series discords is to detect anomalies over time. Time series discords can be identified using several algorithms, each of which uses different techniques for measuring the distance or dissimilarity between subsequences and for identifying the most-anomalous subsequences [47,48]. In addition to the MP method, here are a few examples:…”

Section: Other Time Series Discord Methodsmentioning

confidence: 99%

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

View full text Add to dashboard Cite

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

show abstract

Section: Other Time Series Discord Methodsmentioning

confidence: 99%

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…For example, is high, it indicates a high level of the characteristic associated with depression. The particular fuzzy dominions and how they are combined would reckon on the traits and divisors taken into account [31]. This fuzzy desegregation heightens the model's ability to greet minute variations in user expressions that might be depressive [5], appropriating the model to take into account the imprecision and uncertainty colligated to language expressions consociated with depression.…”

Section: A Encoder Used In Multi-aspect With Fuzzy Logicmentioning

confidence: 99%

Revolutionizing Generalized Anxiety Disorder Detection using a Deep Learning Approach with MGADHF Architecture on Social Media

Alshanketi

2024

IJACSA

View full text Add to dashboard Cite

In the contemporary landscape, social media has emerged as a dominant medium via which individuals are able to articulate a wide range of emotions, encompassing both positive and negative sentiments, therefore offering significant insights into their psychological well-being. The ability to identify these emotional signals plays a vital role in the timely identification of persons who are undergoing depression and other mental health difficulties, hence facilitating the implementation of potentially life-saving therapies. There are already a multitude of clever algorithms available that demonstrate high accuracy in predicting depression. Despite the availability of many machine learning (ML) techniques for detecting persons with depression, the overall effectiveness of these systems has been deemed unsatisfactory. In order to overcome this constraint, the present study introduces an innovative methodology for identifying depression by employing deep learning (DL) techniques, specifically the Deep Learning Multi-Aspect Generalized Anxiety Disorder Detection with Hierarchical-Attention Network and Fuzzy (MGADHF). The process of feature selection is conducted by employing the Adaptive Particle and Grey Wolf optimization techniques and fuzzy. The Multi-Aspect Depression Detection with Hierarchical Attention Network (MDHAN) model is subsequently utilized to categorize Twitter data, differentiating between those exhibiting symptoms of depression and those who do not. Comparative assessments are performed utilizing established methodologies such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Minimum Description Length (MDL), and MDHAN. As proposed, the MGADHF architecture demonstrates a notable accuracy level, reaching 99.19%. This surpasses frequency-based DL models' performance and achieves a reduced false-positive rate.

show abstract

“…In the context of the Internet of Things, wireless sensor networks have such important qualities and specific constraints (Nazarov et al, 2021). Also, the hardware of wireless nodes and network interaction protocols between them are optimized for power consumption to ensure a long service life of the system with autonomous power supplies (DeMedeiros et al, 2023;Kalinin, 2019).…”

Section: Emergence Of the Iotmentioning

confidence: 99%

Current Applications and Challenges of the Internet of Things

Dallaev

Pisarenko

Ţălu

et al. 2023

New Trends in Computer Sciences

View full text Add to dashboard Cite

The concept of the Internet of Things is capable of making a giant leap in the economy, including research in the field of computer science, network technologies, microelectronics and sensor technology. Combined with the technological developments of nanotechnology and robotics, IoT can play a central role in the industrial revolution by creating economic relations between machines and connecting the economy of people and machines, solving a number of problems that humanity is facing. All devices controlled via the Internet are elements of the Internet of things. The IoT has allowed various possibilities for all countries to improve life quality and the technological ideas for efficiency, productivity, security, and profit. An integrated security system is a giant step towards the improved economy. The concept of IoT plays a decisive role in the further development of the infocommunication industry. This is confirmed both by the position of the International Telecommunication Union (ITU) and the European Union on this issue, and by the inclusion of the Internet of Things in the list of breakthrough technologies in the United States, China and other countries. Thus, this article will go over the current state of the IoT as well as describe which devices and industries stand to benefit from the advantages it brings. Additionally, statistical data on the actual trends and investments into IoT across the world are also provided.

show abstract

A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

Cited by 39 publications

References 74 publications

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Revolutionizing Generalized Anxiety Disorder Detection using a Deep Learning Approach with MGADHF Architecture on Social Media

Current Applications and Challenges of the Internet of Things

Contact Info

Product

Resources

About