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Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 13074

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Special Issue Editor

Prof. Dr. Byung-Gyu Kim

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Guest Editor

Department of IT Engineering, Sookmyung Women’s University, Seoul, Korea
Interests: visual sensor network; real-time object segmentation; deep learning for object detection; facial expression recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Starting with Industry 4.0, smart IoT technologies based on intelligence and connectivity are being researched in various industrial fields. To build a fully automated production system, these technologies are evolving in combination with recent artificial intelligence (AI) technologies. While many manufacturers are still busy developing methods for interconnecting new technologies to improve efficiency and productivity—the guiding principle behind Industry 4.0—the next step is being discussed as Industry 5.0. However, there are still ongoing issues that need to be addressed in terms of productivity and cost reduction by helping workers or people, including data acquisition and management, heterogeneous network management, and information extraction in many areas. This also requires integrated mechanisms and data interfaces that can effectively process data and signals from various sensors to give intelligence, and security for critical data is also an important part.

This issue will publish original technical papers and review papers on these recent technologies which are focusing on smart Industrial IoT technologies based on intelligence and connectivity, such as real-time sensor networks, network structure, sensor signal processing, data security scheme, and industrial applications.

You are welcome to submit an unpublished original research work related to the theme of “Sensor Networks and Security Management for Industrial IoT (IIoT) Applications”.

Prof. Dr. Byung-Gyu Kim
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.

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Keywords

Smart sensors
Sensor networks and signal processing
Smart industrial IoT
Heterogeneous network management
Sensor data interfaces
Big data analysis
Intelligent machine learning mechanism for sensor signal processing
Security management

Published Papers (5 papers)

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Research

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17 pages, 444 KiB

Open AccessArticle

Extracting the Secrets of OpenSSL with RAMBleed

by Chihiro Tomita, Makoto Takita, Kazuhide Fukushima, Yuto Nakano, Yoshiaki Shiraishi and Masakatu Morii

Sensors 2022, 22(9), 3586; https://doi.org/10.3390/s22093586 - 09 May 2022

Cited by 2 | Viewed by 1978

Abstract

Concomitant with the increasing density of semiconductors, various attacks that threaten the integrity and security of dynamic random access memory (DRAM) have been devised. Among these, a side-channel attack called RAMBleed is a prolific one that utilizes a general user-level account without special rights to read secret information. Studies have reported that it can be used to obtain OpenSSH secret keys. However, a technique for deriving the Rivest–Shamir–Adleman (RSA) secret keys used in OpenSSL under realistic parameters and environments has not been reported. We propose a method that uses RAMBleed to obtain OpenSSL secret keys and demonstrate its efficacy using the example of an Apache server. The proposed method exploits the fact that, in the operation of an Apache server that uses OpenSSL, the RSA private keys are deployed on DRAM at a set time. Although the result of reading this secret information contains a few errors, error-free secret information is obtainable when it is used with RSA cryptanalysis techniques. We performed a series of attacks incorporating RAMBleed and eventually retrieved the OpenSSL RSA private key, indicating that secret information is obtainable with high probability. The proposed method can easily and externally be executed without administrator privileges on a server using DRAM that is vulnerable to RAMBleed, showing that RAMBleed is also a major threat to OpenSSL. Full article

(This article belongs to the Special Issue Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications)

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15 pages, 4680 KiB

Open AccessArticle

De-Identification Mechanism of User Data in Video Systems According to Risk Level for Preventing Leakage of Personal Healthcare Information

by Jinsu Kim and Namje Park

Sensors 2022, 22(7), 2589; https://doi.org/10.3390/s22072589 - 28 Mar 2022

Cited by 6 | Viewed by 1896

Abstract

A problem with biometric information is that it is more sensitive to external leakage, because it is information that cannot be changed immediately compared to general authentication methods. Regarding facial information, a case in which authentication was permitted by facial information output by a 3D printer was found. Therefore, a method for minimizing the leakage of biometric information to the outside is required. In this paper, different levels of identification information according to the authority of the user are provided by the de-identification of metadata and face information in stages. For face information and metadata, the level of de-identification is determined and achieved according to the risk level of the de-identified subject. Then, we propose a mechanism to minimize the leakage path by preventing reckless data access by classifying access rights to unidentified data according to four roles. The proposed mechanism provides only differentially de-identified data according to the authority of the accessor, and the required time to perform the de-identification of one image was, on average, 3.6 ms for 300 datapoints, 3.5 ms for 500 datapoints, and 3.47 ms for 1000 datapoints. This confirmed that the required execution time was shortened in proportion to the increase in the size of the dataset. The results for the metadata were similar, and it was confirmed that it took 4.3 ms for 300 cases, 3.78 ms for 500 cases, and 3.5 ms for 1000 cases. Full article

(This article belongs to the Special Issue Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications)

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16 pages, 1878 KiB

Open AccessArticle

Reinforced Palmprint Reconstruction Attacks in Biometric Systems

by Yue Sun, Lu Leng, Zhe Jin and Byung-Gyu Kim

Sensors 2022, 22(2), 591; https://doi.org/10.3390/s22020591 - 13 Jan 2022

Cited by 4 | Viewed by 1800

Abstract

Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods. Full article

(This article belongs to the Special Issue Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications)

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Review

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33 pages, 3385 KiB

Open AccessReview

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0

by Bassam Zafar and Sami Ben Slama

Sensors 2022, 22(21), 8397; https://doi.org/10.3390/s22218397 - 01 Nov 2022

Cited by 9 | Viewed by 2447

Abstract

The Energy Internet (EI) and Smart Grid 2.0 (SG 2.0) concepts are potential challenges in industry and research. The purpose of SG 2.0 and EI is to automate innovative power grid operations. To move from Distribution Network Operators (DSO) to consumer-centric distributed power grid management, the blockchain and smart contracts are applicable. Blockchain technology and integrated SGs will present challenges, limiting the deployment of Distributed Energy Resources (DERs). This review looks at the decentralization of the Smart Grid 2.0 using blockchain technology. Energy trading has increased due to access to distributed energy sources and electricity producers who can financially export surplus fuels. The energy trading system successfully combines energy from multiple sources to ensure consistent and optimal use of available resources and better facilities for energy users. Peer-to-peer (P2P) energy trading is a common field of study that presents some administrative and technical difficulties. This article provides a general overview of P2P energy exchange. It discusses how blockchain can improve transparency and overall performance, including the degree of decentralization, scalability, and device reliability. The research is extended to examine unresolved issues and potential directions for P2P blockchain-based energy sharing in the future. In fact, this paper also demonstrates the importance of blockchain in future smart grid activities and its blockchain-based applications. The study also briefly examines the issues associated with blockchain integration, ensuring the decentralized, secure and scalable operation of autonomous electric grids in the future. Full article

(This article belongs to the Special Issue Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications)

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40 pages, 4594 KiB

Open AccessReview

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

by Sami Saeed Binyamin and Sami Ben Slama

Sensors 2022, 22(21), 8099; https://doi.org/10.3390/s22218099 - 22 Oct 2022

Cited by 6 | Viewed by 3818

Abstract

Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled. Full article

(This article belongs to the Special Issue Smart Sensor Networks and Security Management for Industrial IoT (IIoT) Applications)

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