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Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023

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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 (30 September 2023) | Viewed by 26157

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Prof. Dr. Byung-Seo Kim

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Guest Editor

Department of Software and Communications Energy, Hongik University, Sejongro 2639, Republic of Korea
Interests: wireless networks and communications; WSN; wireless ICN; edge computing
Special Issues, Collections and Topics in MDPI journals

Dr. Rehmat Ullah

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Guest Editor

School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
Interests: federated learning; sensor networks; edge computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Muhammad Khalil Afzal

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Guest Editor

COMSATS Institute of Information Technology, GT Road, Wah Cantt, Pakistan
Interests: wireless sensor networks; ad hoc networks; Smart Cities; 5G; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th International Conference on Green and Human Information Technology (ICGHIT 2022) will be held 19–22 January, 2021, on Jeju Island, Korea (http://icghit.org/).

The 10th International Conference on Green and Human Information Technology is a unique global conference for researchers, industry professionals, and academics who are interested in the latest development of green and human information technology. The main theme of ICGHIT this year is “Emerging AI+X technology”, in which “X” indicates any applied science area (network, signal processing, circuit, telecommunications, etc.). The latest AI+X technologies are already pervading our daily life regardless of our recognition and present us with major challenges and great opportunities at the same time.

Centering around the main theme, ICGHIT will provide an exciting program: hands-on experience-based tutorial sessions and special sessions covering research issues and directions with applications from both theoretical and practical viewpoints. The conference will also include plenary sessions, technical sessions, and workshops with special sessions.

Highly qualified papers selected from ICGHIT 2021 will be invited to submit to this Special Issue for publication. However, the Special Issue also welcomes submissions from general researchers which fit within the scope of the SI as shown below.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Green Sensor-based Information Technology

Green Technology and Energy Saving;
Green Computing and Green IT Convergence and Applications;
Energy-Harvest-based Communications and Networking;
Technologies for Network Sustainability.

Communications and Networks for IoT and 5G-based wireless sensor network.

Communications and Networks for Massive IoT and 5G;
Optical and Visual Light Communication;
Ad-hoc and Sensor Networks;
M2M/IoT and Ubiquitous Computing;
NFV, SDN, ICN, Network Slicing;
AI- and ML-based Technologies.

Sensor Network Security

Block-Chain-based Networking and Applications;
Distributed PKI;
Applied Cryptography;
Security in Big Data and Cloud Computing;
Security for Future Internet Architecture (SDN, ICN, etc.).

Control and Intelligent Sensor System

Automatic Control and Neural Network and Fuzzy;
Artificial Intelligence and HCI;
Intelligent Robotics, Transportation and HRI;
Brain Science and Bioengineering.

Network SW/HW Design, Architecture and Applications

Architecture and Protocols;
Sustainable Sensor Networks;
Information-centric Sensor Networks;
Blockchain-based Secure Sensor Networks;
AI-based Self-evolving Sensor networks;
Sensor/RFID Circuit Design;
System on Chip (SoC);
IC System for Communication.

We look forward to receiving your contributions.

Prof. Dr. Byung-Seo Kim
Dr. Rehmat Ullah
Prof. Dr. Muhammad Khalil Afzal
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.

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Published Papers (11 papers)

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Editorial

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4 pages, 145 KiB

Open AccessEditorial

Special Issue “Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023”

by Byung-Seo Kim, Muhammad Khalil Afzal and Rehmat Ullah

Sensors 2024, 24(2), 546; https://doi.org/10.3390/s24020546 - 15 Jan 2024

Viewed by 1065

Abstract

This Special Issue is a collection of selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs) [...] Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

Research

Jump to: Editorial

11 pages, 677 KiB

Open AccessArticle

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum

by Younghoon Park and Yejin Kim

Sensors 2023, 23(24), 9763; https://doi.org/10.3390/s23249763 - 11 Dec 2023

Viewed by 1350

Abstract

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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12 pages, 4044 KiB

Open AccessArticle

LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function

by Do-Hyun Park, Min-Wook Jeon, Da-Min Shin and Hyoung-Nam Kim

Sensors 2023, 23(20), 8564; https://doi.org/10.3390/s23208564 - 18 Oct 2023

Cited by 6 | Viewed by 2182

Abstract

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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28 pages, 4644 KiB

Open AccessArticle

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs

by Amalia Amalia, Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Kyusung Shim and Beongku An

Sensors 2023, 23(19), 8224; https://doi.org/10.3390/s23198224 - 2 Oct 2023

Cited by 4 | Viewed by 1360

Abstract

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node’s remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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28 pages, 2172 KiB

Open AccessArticle

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output

by Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Kyusung Shim and Beongku An

Sensors 2023, 23(18), 7960; https://doi.org/10.3390/s23187960 - 18 Sep 2023

Cited by 1 | Viewed by 1560

Abstract

The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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25 pages, 587 KiB

Open AccessArticle

Secrecy Performance Analysis of Cooperative Multihop Transmission for WSNs under Eavesdropping Attacks

by Yosefine Triwidyastuti, Ridho Hendra Yoga Perdana, Kyusung Shim and Beongku An

Sensors 2023, 23(17), 7653; https://doi.org/10.3390/s23177653 - 4 Sep 2023

Cited by 1 | Viewed by 1204

Abstract

Multihop transmission is one of the important techniques to overcome the transmission coverage of each node in wireless sensor networks (WSNs). However, multihop transmission has a security issue due to the nature of a wireless medium. Additionally, the eavesdropper also attempts to interrupt the legitimate users’ transmission. Thus, in this paper, we study the secrecy performance of a multihop transmission under various eavesdropping attacks for WSNs. To improve the secrecy performance, we propose two node selection schemes in each cluster, namely, minimum node selection (MNS) and optimal node selection (ONS) schemes. To exploit the impact of the network parameters on the secrecy performance, we derive the closed-form expression of the secrecy outage probability (SOP) under different eavesdropping attacks. From the numerical results, the ONS scheme shows the most robust secrecy performance compared with the other schemes. However, the ONS scheme requires a lot of channel information to select the node in each cluster and transmit information. On the other side, the MNS scheme can reduce the amount of channel information compared with the ONS scheme, while the MNS scheme still provides secure transmission. In addition, the impact of the network parameters on the secrecy performance is also insightfully discussed in this paper. Moreover, we evaluate the trade-off of the proposed schemes between secrecy performance and computational complexity. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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18 pages, 6389 KiB

Open AccessArticle

Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks

by Kithmini Godewatte Arachchige, Philip Branch and Jason But

Sensors 2023, 23(14), 6265; https://doi.org/10.3390/s23146265 - 10 Jul 2023

Cited by 7 | Viewed by 1599

Abstract

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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22 pages, 1396 KiB

Open AccessArticle

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

by Asif Mehmood, Kyu-Tae Lee and Do-Hyeun Kim

Sensors 2023, 23(7), 3640; https://doi.org/10.3390/s23073640 - 31 Mar 2023

Cited by 8 | Viewed by 6083

Abstract

Home appliances are considered to account for a large portion of smart homes’ energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances’ energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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18 pages, 6093 KiB

Open AccessArticle

IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

by Md. Moklesur Rahman and Heung-Gyoon Ryu

Sensors 2022, 22(19), 7341; https://doi.org/10.3390/s22197341 - 27 Sep 2022

Cited by 5 | Viewed by 2251

Abstract

Wireless sensor networks (WSNs) systems based on Internet of Things (IoT) have developed rapidly in recent years. However, interference is a major obstacle to relatively long-distance communications in such networks. It is also very complicated and challenging to fix the exact location of tags in the IoT sensor networks. To overcome these problems, in this paper, an electronic steering parasitic array radiator (ESPAR) antenna used as a beamformer to handle the interference and extend the communication range from the sensors or tags is suggested. In addition, an efficient method, namely beam scanning (BS), is proposed to find the directions of tags. The beam scanning method (BSM) can be used for the selective beam switching (SBS) system by designing an ESPAR or array of ESPAR antennas with the help of CST studio. The antennas exhibit higher gain (8.17 dBi, 11.40 dBi) and proper radiation pattern at a particular direction. In addition, the MATLAB simulation findings indicate that the proposed BSM algorithm provides longer communication range, i.e., 25 m. In order to maximize range while avoiding interference, it is necessary to determine the direction and precise orientation of the tag in the WSN communication systems. Consequently, this work could be applied to an IoT sensor network such as an electrocardiogram system by providing better advantages such as higher localization accuracy and longer operating range. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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22 pages, 1863 KiB

Open AccessArticle

A Hybrid Price Auction-Based Secure Routing Protocol Using Advanced Speed and Cosine Similarity-Based Clustering against Sinkhole Attack in VANETs

by Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Thong-Nhat Tran, Kyusung Shim and Beongku An

Sensors 2022, 22(15), 5811; https://doi.org/10.3390/s22155811 - 3 Aug 2022

Cited by 11 | Viewed by 2072

Abstract

In ad-hoc vehicle networks (VANETs), the random mobility causes the rapid network topology change, which leads to the challenge of the reliable data transmission. In this paper, we propose a hybrid-price auction-based secure routing (HPA-SR) protocol using advanced speed and cosine similarity-based (ASCS) clustering to establish a secure route to avoid sinkhole attacks and improve connectivity between nodes. The main features and contributions of the proposed HPA-SR protocol are as follows. First, the HPA-SR protocol is employed by the first- and second-price auctions to avoid sinkhole attacks. More specifically, using the Markov decision process (MDP), each node can select a kind of auction method to establish the secure route by avoiding the sinkhole attack. Second, the advanced speed cosine similarity clustering protocol that is considered as underlying structure is presented to improve the connectivity between nodes. The ASCS is constructed based on the cosine similarity and distance between nodes using the speed and direction of the nodes. The results of the performance show that the proposed HPA-SR protocol can establish the secure route avoiding the sinkhole attack while the proposed ASCS clustering can support the strong connectivity. Besides, the HPA-SR with ASCS protocol can show better performance than the benchmark protocol in terms of the routing delay, packet loss ratio, number of packet loss, and control overhead. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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17 pages, 507 KiB

Open AccessArticle

GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices

by Nabeela Kausar, Ikram Ud Din, Mudassar Ali Khan, Ahmad Almogren and Byung-Seo Kim

Sensors 2022, 22(4), 1349; https://doi.org/10.3390/s22041349 - 10 Feb 2022

Cited by 9 | Viewed by 3638

Abstract

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users’ identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful. Full article

(This article belongs to the Special Issue Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023)

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