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New Trends in Smart Sensor Networks, Smart Computing, and Network Security

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

Deadline for manuscript submissions: closed (24 October 2021) | Viewed by 28230

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Special Issue Editors

Prof. Dr. Juan Francisco De Paz Santana

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Guest Editor

Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: ambient intelligence; artificial intelligence; multi-agent systems; wireless sensor networks; big data; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Cristian Pinzón Trejos

E-Mail Website
Guest Editor

Robotics, Intelligent Systems and Simulation Research Group, Veraguas Regional Center, Technological University of Panama, San Antonio, Atalaya, Panama City 507, Panamá
Interests: artificial intelligence; multiagent systems; wireless sensor networks; Internet of Things; robotics; datamining

Prof. Dr. Lixin Wang

E-Mail Website
Guest Editor

TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: network security; intrusion detection systems; wireless networks; algorithm design and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New trends in sensors networks and communication protocols have allowed the development of new proposals in the Internet of Things (IoT). The emergence of new smart sensors has involved the search for new computing paradigms to analyze information efficiently in accordance with processing and transmission. Today, smart sensors usually have low computing capacity, and it is necessary to use light processing techniques that allow efficient computing with low data transmission in order to support decision making. Network security has become more important to personal computer users, organizations, and the military. It is one of the most important aspects to consider when working over the Internet, LAN, or other types of networks. For this reason, it is necessary to search or use new intelligent computing systems such as edge computing and fog computing. This Special Issue will focus on the use of new trends in smart sensor networks and processing techniques in different cases studies, and new approaches to secure computer networks. We invite you to submit your contributions on software/hardware developments, reviews and cases studies with relevant contributions in new trends in smart sensors, security in IoT, and new computing paradigms.

Topics of interest include but are not limited to:

Security in IoT
Smart sensors
Mobile sensing machine learning applications
Intelligent environment with sensor networks
Artificial Intelligence in edge and fog computing
Information fusion in edge and fog computing
System architecture for mobile sensing
Fog intelligent computing
Edge intelligent computing
Smart environments
Smart control in robotics
Low latency communication
Blockchain
Intrusion detection and prevention
Security aspects of the Internet

Dr. Juan Francisco De Paz Santana
Dr. Cristian Pinzón Trejos
Dr. Lixin Wang
Guest Editors

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

IoT
Smart environments
Edge computing
Fog computing
Blockchain
Network Security

Published Papers (6 papers)

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Research

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18 pages, 1522 KiB

Open AccessArticle

Applying MMD Data Mining to Match Network Traffic for Stepping-Stone Intrusion Detection

by Jianhua Yang and Lixin Wang

Sensors 2021, 21(22), 7464; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227464 - 10 Nov 2021

Cited by 2 | Viewed by 1425

Abstract

A long interactive TCP connection chain has been widely used by attackers to launch their attacks and thus avoid detection. The longer a connection chain, the higher the probability the chain is exploited by attackers. Round-trip Time (RTT) can represent the length of a connection chain. In order to obtain the RTTs from the sniffed Send and Echo packets in a connection chain, matching the Sends and Echoes is required. In this paper, we first model a network traffic as the collection of RTTs and present the rationale of using the RTTs of a connection chain to represent the length of the chain. Second, we propose applying MMD data mining algorithm to match TCP Send and Echo packets collected from a connection. We found that the MMD data mining packet-matching algorithm outperforms all the existing packet-matching algorithms in terms of packet-matching rate including sequence number-based algorithm, Yang’s approach, Step-function, Packet-matching conservative algorithm and packet-matching greedy algorithm. The experimental results from our local area networks showed that the packet-matching accuracy of the MMD algorithm is 100%. The average packet-matching rate of the MMD algorithm obtained from the experiments conducted under the Internet context can reach around 94%. The MMD data mining packet-matching algorithm can fix the issue of low packet-matching rate faced by all the existing packet-matching algorithms including the state-of-the-art algorithm. It is applicable to network-based stepping-stone intrusion detection. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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18 pages, 6192 KiB

Open AccessArticle

Underground Microseismic Event Monitoring and Localization within Sensor Networks

by Sili Wang, Mark P. Panning, Steven D. Vance and Wenzhan Song

Sensors 2021, 21(8), 2830; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082830 - 17 Apr 2021

Cited by 1 | Viewed by 1583

Abstract

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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17 pages, 1681 KiB

Open AccessArticle

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

by Maghsoud Morshedi and Josef Noll

Sensors 2021, 21(2), 621; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020621 - 17 Jan 2021

Cited by 7 | Viewed by 2752

Abstract

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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23 pages, 1397 KiB

Open AccessArticle

PRISEC: Comparison of Symmetric Key Algorithms for IoT Devices

by Daniel A. F. Saraiva, Valderi Reis Quietinho Leithardt, Diandre de Paula, André Sales Mendes, Gabriel Villarrubia González and Paul Crocker

Sensors 2019, 19(19), 4312; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194312 - 05 Oct 2019

Cited by 58 | Viewed by 7084

Abstract

With the growing number of heterogeneous resource-constrained devices connected to the Internet, it becomes increasingly challenging to secure the privacy and protection of data. Strong but efficient cryptography solutions must be employed to deal with this problem, along with methods to standardize secure communications between these devices. The PRISEC module of the UbiPri middleware has this goal. In this work, we present the performance of the AES (Advanced Encryption Standard), RC6 (Rivest Cipher 6), Twofish, SPECK128, LEA, and ChaCha20-Poly1305 algorithms in Internet of Things (IoT) devices, measuring their execution times, throughput, and power consumption, with the main goal of determining which symmetric key ciphers are best to be applied in PRISEC. We verify that ChaCha20-Poly1305 is a very good option for resource constrained devices, along with the lightweight block ciphers SPECK128 and LEA. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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18 pages, 1357 KiB

Open AccessArticle

PRISER: Managing Notification in Multiples Devices with Data Privacy Support

by Luis Augusto Silva, Valderi Reis Quietinho Leithardt, Carlos O. Rolim, Gabriel Villarrubia González, Cláudio F. R. Geyer and Jorge Sá Silva

Sensors 2019, 19(14), 3098; https://0-doi-org.brum.beds.ac.uk/10.3390/s19143098 - 13 Jul 2019

Cited by 25 | Viewed by 5048

Abstract

With the growing number of mobile devices receiving daily notifications, it is necessary to manage the variety of information produced. New smart devices are developed every day with the ability to generate, send, and display messages about their status, data, and information about other devices. Consequently, the number of notifications received by a user is increasing and their tolerance may decrease in a short time. With this, it is necessary to develop a management system and notification controls. In this context, this work proposes a notification and alert management system called PRISER. Its focus is on user profiles and environments, applying data privacy criteria. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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Review

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22 pages, 4119 KiB

Open AccessEditor’s ChoiceReview

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

by Zhenyi Ye, Yuan Liu and Qiliang Li

Sensors 2021, 21(22), 7620; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227620 - 16 Nov 2021

Cited by 44 | Viewed by 8751

Abstract

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation. Full article

(This article belongs to the Special Issue New Trends in Smart Sensor Networks, Smart Computing, and Network Security)

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