Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Selected Papers from WISA 2020

Special Issue Editors
Special Issue Information
Published Papers

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 46175

Share This Special Issue

Special Issue Editor

Dr. Ilsun You

E-Mail Website
Guest Editor

Department of Information Security Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si 31538, Choongchungnam-do, Republic of Korea
Interests: 5G security; IoT security; authentication and access control; formal security analysis; mobile internet security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

WISA is one of the main security research venues hosted by the Korea Institute of Information Security and Cryptology (KIISC) and sponsored by the Ministry of Science, ICT and Future Planning (MSIP), and co-sponsored by the Electronics and Telecommunications Research Institute (ETRI), the Korea Internet and Security Agency (KISA), and the National Security Research Institute (NSRI). The primary focus of WISA 2020 will be on systems and network security, including all other technical and practical aspects of security applications. This Special Issue will include extended versions of selected papers from WISA 2020, along with general papers closely related to the conference themes. Potential topics include but are not limited to:

Analysis of network and security protocols;
Anonymity and censorship-resistant technologies;
Applications of cryptographic techniques;
Authentication and authorization;
Automated tools for source code/binary analysis;
Automobile security;
Botnet defense;
Blockchain security;
Critical infrastructure security;
Denial-of-service attacks and countermeasures;
Digital forensics;
Embedded systems security;
Exploit techniques and automation;
Hardware and physical security;
HCI security and privacy;
Intrusion detection and prevention;
Malware analysis;
Mobile/wireless/cellular system security;
Network-based attacks;
Network infrastructure security;
Operating system security;
Practical cryptanalysis (hardware, DRM, etc.);
Security policy;
Side channel attacks and countermeasures;
Storage and file systems security;
Techniques for developing secure systems;
Trustworthy computing;
Trusted execution environments;
Unmanned system security for vehicle/drone/ship systems;
Vulnerability research;
Web security

Prof. Dr. Ilsun You
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.

Published Papers (13 papers)

Download All Papers

Research

35 pages, 9513 KiB

Open AccessArticle

Can Formal Security Verification Really Be Optional? Scrutinizing the Security of IMD Authentication Protocols

by Daniel Gerbi Duguma, Ilsun You, Yonas Engida Gebremariam and Jiyoon Kim

Sensors 2021, 21(24), 8383; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248383 - 15 Dec 2021

Cited by 1 | Viewed by 2173

Abstract

The need for continuous monitoring of physiological information of critical organs of the human body, combined with the ever-growing field of electronics and sensor technologies and the vast opportunities brought by 5G connectivity, have made implantable medical devices (IMDs) the most necessitated devices in the health arena. IMDs are very sensitive since they are implanted in the human body, and the patients depend on them for the proper functioning of their vital organs. Simultaneously, they are intrinsically vulnerable to several attacks mainly due to their resource limitations and the wireless channel utilized for data transmission. Hence, failing to secure them would put the patient’s life in jeopardy and damage the reputations of the manufacturers. To date, various researchers have proposed different countermeasures to keep the confidentiality, integrity, and availability of IMD systems with privacy and safety specifications. Despite the appreciated efforts made by the research community, there are issues with these proposed solutions. Principally, there are at least three critical problems. (1) Inadequate essential capabilities (such as emergency authentication, key update mechanism, anonymity, and adaptability); (2) heavy computational and communication overheads; and (3) lack of rigorous formal security verification. Motivated by this, we have thoroughly analyzed the current IMD authentication protocols by utilizing two formal approaches: the Burrows–Abadi–Needham logic (BAN logic) and the Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compared these schemes against their security strengths, computational overheads, latency, and other vital features, such as emergency authentications, key update mechanisms, and adaptabilities. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

25 pages, 2788 KiB

Open AccessArticle

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

by Borja Bordel, Ramón Alcarria and Tomás Robles

Sensors 2021, 21(21), 7301; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217301 - 02 Nov 2021

Cited by 2 | Viewed by 1301

Abstract

Industry 4.0 is envisioned to transform the entire economical ecosystem by the inclusion of new paradigms, such as cyber-physical systems or artificial intelligence, into the production systems and solutions. One of the main benefits of this revolution is the increase in the production systems’ efficiency, thanks to real-time algorithms and automatic decision-making mechanisms. However, at the software level, these innovative algorithms are very sensitive to the quality of received data. Common malfunctions in sensor nodes, such as delays, numerical errors, corrupted data or inactivity periods, may cause a critical problem if an inadequate decision is made based on those data. Many systems remove this risk by seamlessly integrating the sensor nodes and the high-level components, but this situation substantially reduces the impact of the Industry 4.0 paradigm and increases its deployment cost. Therefore, new solutions that guarantee the interoperability of all sensors with the software elements in Industry 4.0 solutions are needed. In this paper, we propose a solution based on numerical algorithms following a predictor-corrector architecture. Using a combination of techniques, such as Lagrange polynomial and Hermite interpolation, data series may be adapted to the requirements of Industry 4.0 software algorithms. Series may be expanded, contracted or completed using predicted samples, which are later updated and corrected using the real information (if received). Results show the proposed solution works in real time, increases the quality of data series in a relevant way and reduces the error probability in Industry 4.0 systems. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

18 pages, 16396 KiB

Open AccessArticle

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

by Yongsu Kim, Hyoeun Kang, Naufal Suryanto, Harashta Tatimma Larasati, Afifatul Mukaroh and Howon Kim

Sensors 2021, 21(16), 5323; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165323 - 06 Aug 2021

Cited by 3 | Viewed by 2809

Abstract

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

20 pages, 5765 KiB

Open AccessArticle

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

by Karisma Trinanda Putra, Hsing-Chung Chen, Prayitno, Marek R. Ogiela, Chao-Lung Chou, Chien-Erh Weng and Zon-Yin Shae

Sensors 2021, 21(13), 4586; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134586 - 04 Jul 2021

Cited by 28 | Viewed by 3897

Abstract

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

23 pages, 1111 KiB

Open AccessArticle

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

by Seong-Yun Jeon and Mun-Kyu Lee

Sensors 2021, 21(8), 2859; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082859 - 19 Apr 2021

Cited by 3 | Viewed by 2056

Abstract

With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

20 pages, 1262 KiB

Open AccessArticle

Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain

by Muhammad Firdaus, Sandi Rahmadika and Kyung-Hyune Rhee

Sensors 2021, 21(7), 2410; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072410 - 31 Mar 2021

Cited by 24 | Viewed by 3511

Abstract

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

17 pages, 749 KiB

Open AccessArticle

Unsupervised Fault Detection on Unmanned Aerial Vehicles: Encoding and Thresholding Approach

by Kyung Ho Park, Eunji Park and Huy Kang Kim

Sensors 2021, 21(6), 2208; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062208 - 22 Mar 2021

Cited by 25 | Viewed by 3849

Abstract

Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle’s control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a finely-labeled training dataset. Moreover, both prior approaches commonly include a limit that the detection model can identify the trained type of faults only, but fail to recognize the unseen type of faults. In pursuit of resolving the aforementioned drawbacks, we proposed a fault detection model utilizing a stacked autoencoder that lies under unsupervised learning. The autoencoder was trained with data from safe UAV states, and its reconstruction loss was examined to distinguish the safe states and faulty states. The key contributions of our study are, as follows. First, we presented a series of analyses to extract essential features from raw UAV flight logs. Second, we designed a fault detection model consisting of the stacked autoencoder and the classifier. Lastly, we validated our approach’s fault detection performance with two datasets consisting of different types of UAV faults. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

16 pages, 16871 KiB

Open AccessArticle

Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

by Moohong Min, Jemin Justin Lee, Hyunbeom Park and Kyungho Lee

Sensors 2021, 21(6), 2039; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062039 - 13 Mar 2021

Cited by 5 | Viewed by 4759

Abstract

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

26 pages, 1355 KiB

Open AccessArticle

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption

by Turabek Gaybullaev, Hee-Yong Kwon, Taesic Kim and Mun-Kyu Lee

Sensors 2021, 21(6), 2024; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062024 - 12 Mar 2021

Cited by 16 | Viewed by 3068

Abstract

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

17 pages, 5257 KiB

Open AccessArticle

Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme

by Semi Park and Kyungho Lee

Sensors 2021, 21(6), 1976; https://0-doi-org.brum.beds.ac.uk/10.3390/s21061976 - 11 Mar 2021

Cited by 5 | Viewed by 2470

Abstract

Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

16 pages, 7646 KiB

Open AccessArticle

Improving Real-Time Hand Gesture Recognition with Semantic Segmentation

by Gibran Benitez-Garcia, Lidia Prudente-Tixteco, Luis Carlos Castro-Madrid, Rocio Toscano-Medina, Jesus Olivares-Mercado, Gabriel Sanchez-Perez and Luis Javier Garcia Villalba

Sensors 2021, 21(2), 356; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020356 - 07 Jan 2021

Cited by 37 | Viewed by 5419

Abstract

Hand gesture recognition (HGR) takes a central role in human–computer interaction, covering a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ multiple modalities derived from RGB input frames, such as optical flow. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the optical flow computation by proposing a real-time hand gesture recognition method based on RGB frames combined with hand segmentation masks. We employ a light-weight semantic segmentation method (FASSD-Net) to boost the accuracy of two efficient HGR methods: Temporal Segment Networks (TSN) and Temporal Shift Modules (TSM). We demonstrate the efficiency of the proposal on our IPN Hand dataset, which includes thirteen different gestures focused on interaction with touchless screens. The experimental results show that our approach significantly overcomes the accuracy of the original TSN and TSM algorithms by keeping real-time performance. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

29 pages, 2754 KiB

Open AccessArticle

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

by Gildásio Antonio de Oliveira Júnior, Robson de Oliveira Albuquerque, César Augusto Borges de Andrade, Rafael Timóteo de Sousa, Jr., Ana Lucila Sandoval Orozco and Luis Javier García Villalba

Sensors 2020, 20(16), 4557; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164557 - 14 Aug 2020

Cited by 12 | Viewed by 3966

Abstract

Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

13 pages, 1067 KiB

Open AccessArticle

An Android Inline Hooking Framework for the Securing Transmitted Data

by Yu-an Tan, Shuo Feng, Xiaochun Cheng, Yuanzhang Li and Jun Zheng

Sensors 2020, 20(15), 4201; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154201 - 28 Jul 2020

Cited by 5 | Viewed by 5111

Abstract

Information leaks can occur through many Android applications, including unauthorized access to sensors data. Hooking is an important technique for protecting Android applications and add security features to them even without its source code. Various hooking frameworks are developed to intercept events and process their own specific events. The hooking tools for Java methods are varied, however, the native hook has few methods. Besides, the commonly used Android hook frameworks cannot meet the requirement of hooking the native methods in shared libraries on non-root devices. Even though some approaches are able to hook these methods, they have limitations or are complicated to implement. In the paper, a feasible hooking approach for Android native methods is proposed and implemented, which does not need any modifications to both the Android framework and app’s code. In this approach, the method’s reference address is modified and control flow is redirected. Beyond that, this study combines this approach with VirtualXposed which aims to run it without root privileges. This hooking framework can be used to enforce security policies and monitor sensitive methods in shared objects. The evaluation of the scheme demonstrates its capability to perform hook operation without a significant runtime performance overhead on real devices and it is compatible and functional for the native hook. Full article

(This article belongs to the Special Issue Selected Papers from WISA 2020)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Selected Papers from WISA 2020

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI