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Artificial Intelligence and IoT technologies for Sensors

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

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 7523

Special Issue Editors


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Guest Editor
Department of Engineering, School of Engineering and Technology, Universidad Internacional de la Rioja (UNIR), Logroño, Spain
Interests: soft computing; accessibility; Artificial Intelligence; learning analytics
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
MDE Research Group, Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain
Interests: internet of things; networks; wireless sensor networks; smart devices coordination and communication; ubiquitous computing and distributed services and applications

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Co-Guest Editor
1. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
2. School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: Internet of Things; sensor networks; green computing; cloud and fog computing; fault diagnosis; wireless sensor networks; multimedia communication; middleware; security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to compile original and high-quality research works related to innovative solutions in the field of artificial intelligence and IoT technologies for sensors. Sensors are the key to obtain information from the physics world, they are applied in all work areas of our society, namely: food, logistics, medicine, construction, industry, and so on. The demand for systems that imply the use of sensors is getting bigger every day, which is why we need better sensors—more efficient, more precious, more intelligent, better communication capabilities, and so on. There are many ways to improve sensor quality, one is by improving the sensor intelligence to create a new kind of smart sensor. We plan to include 8–14 papers in the Special Issue, which will present the works of researchers belonging to relevant universities around the world. Only the highest quality papers will be included in this Special Issue. We will have a thorough review process, which will include reliable researchers with a lot of experience in the specific topics, namely: smart sensors, artificial intelligence techniques applied to sensors, sensor data fusion, artificial intelligence techniques applied to security in sensors, intelligence in sensor networks, smart methods for fault tolerance in sensors, smart methods for sensor maintenance, and sensors in virtual reality applications of smart sensors in IoT.

Dr. Rubén González Crespo
Dr. Jordán Pascual Espada
Prof. Dr. Lei Shu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

  • smart sensors
  • artificial intelligence
  • virtual reality
  • IoT

Published Papers (2 papers)

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Research

31 pages, 2430 KiB  
Article
Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics
by Katherinne Shirley Huancayo Ramos, Marco Antonio Sotelo Monge and Jorge Maestre Vidal
Sensors 2020, 20(16), 4501; https://doi.org/10.3390/s20164501 - 12 Aug 2020
Cited by 34 | Viewed by 4405
Abstract
Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial [...] Read more.
Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns. Full article
(This article belongs to the Special Issue Artificial Intelligence and IoT technologies for Sensors)
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18 pages, 1267 KiB  
Article
A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory
by Peng Zhang, Zhenjiang Zhang and Han-Chieh Chao
Sensors 2020, 20(14), 4016; https://doi.org/10.3390/s20144016 - 19 Jul 2020
Cited by 3 | Viewed by 2174
Abstract
As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by [...] Read more.
As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training. Full article
(This article belongs to the Special Issue Artificial Intelligence and IoT technologies for Sensors)
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