Secure, Efficient Cyber-Physical Systems and Wireless Sensors

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Security and Privacy".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 8114

Special Issue Editors


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Guest Editor
Industrial Systems Institute, ATHENA Research Center, PSP Building, 26501 Patras, Greece
Interests: hardware-software codesign; cybersecurity; cryptographic engineering; wireless sensor network and cyberphysical systems security; side channel attacks; microarchitectural attacks

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Guest Editor
Industrial Systems Institute, ATHENA Research Center, PSP Building, 26501 Patras, Greece
Interests: signal processing and learning: adaptive filtering, numerical linear algebra in signal processing, sparse modeling and optimization, signal processing on graphs, accelerated deep learning; multimedia analysis and multimedia communications: 3D/4D compression, denoising, completion, saliency map extraction, scalable rendering

E-Mail Website
Guest Editor
Università della Svizzera italiana Lugano, Switzerland
Interests: Security of embedded systems and cyber-physical systems (especially side channel attacks); hardware trojans; post-quantum cryptography; lightweight cryptography; design automation for security

Special Issue Information

Dear Colleagues,

The next generation of industrial systems—including energy systems, transportation networks, manufacturing systems, critical infrastructures and large buildings—consists of cyber components that interact directly with the physical world, thus constituting cyberphysical systems (CPS). The need for efficient computation in such systems, considering the fact that they increasingly accumulate intelligent functions, is considerable. The same can be said for their need for high security, since the type of processing they are performing as well as the impact of this processing can be significant if it is maliciously altered. In industrial environments, the above issues can be further localized in the wireless sensor devices that are spread out in factory infrastructures. The efficiency and security challenges in such subdomains are even more hard to solve, since the need for low power consumption and the lack of computing resources in wireless sensor devices is considerable.

The aim of this Special Issue is to bring together researchers and practitioners from diverse fields of science and engineering working on achieving efficiency and/or security in the cyberphysical system domain and the industrial wireless sensors network domain. We invite authors to submit original research articles related to recent advances and related technologies. We are particularly interested in presenting emerging technologies related to autonomic solutions capable of guaranteeing the overall reliability, efficiency and security of CPSs, even when the components or subsystems are not fully reliable and unforeseen conditions emerge during the course of operation. We are open to papers addressing a broad range of topics, from foundational topics regarding the operating principles and novel design principles for building future intelligent CPSs and associated wireless sensors, to papers presenting advanced frameworks and technological platforms, to pilots reporting innovative real-world deployments. 

The Special Issue is supported by the EU H2020 project CPSoSaware: Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS under contract 871738. The CPSoSaware project (http://www.cpsosaware.eu ) develops models and software tools to describe a CPSoS in a holistic and abstract way and to allocate computational power/resources to the CPS end devices of the system by determining and generating autonomously what cyber-physical processes will be handled by devices’ heterogeneous components (processor cores, GPUs, FPGA fabric) and software components (software stacks).

Topics of interest for the Special Issue include (but are not limited to):

  • Distributed, cooperative signal processing and machine learning for dependable CPSs.
  • Augmented reality tools for increasing situational awareness in CPHSs.
  • The design and implementation of smart dynamic network structures for dependable CPSs.
  • Run time security monitoring solutions for CPSoS.
  • CPS and wireless sensor network Security vulnerabilities and countermeasures.
  • CPS hardware/software partitioning for efficiency and reliability.
  • CPS hardware and software design for efficiency and/or security.
  • CPS and wireless sensor modeling for real world applications.
  • Deep multi-modal learning accelerators for the real time monitoring of physical processes.
  • Real-world CPS deployments; pilots of intelligent distributed sensing methods utilizing edge-computing.
  • Efficient wireless sensor designs and realizations
  • Cybersecurity aspects on CPSs and wireless sensor networks

Dr. Apostolos Fournaris
Dr. Aris Lalos
Dr. Francesco Regazzoni
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. Journal of Sensor and Actuator Networks 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 2000 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

  • cyberphysical systems
  • security
  • efficiency
  • wireless sensor networks

Published Papers (2 papers)

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Research

23 pages, 741 KiB  
Article
Machine Learning Attacks and Countermeasures on Hardware Binary Edwards Curve Scalar Multipliers
by Charis Dimopoulos, Apostolos P. Fournaris and Odysseas Koufopavlou
J. Sens. Actuator Netw. 2021, 10(3), 56; https://doi.org/10.3390/jsan10030056 - 16 Aug 2021
Cited by 1 | Viewed by 3388
Abstract
Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively [...] Read more.
Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy. Full article
(This article belongs to the Special Issue Secure, Efficient Cyber-Physical Systems and Wireless Sensors)
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21 pages, 5573 KiB  
Article
Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves
by Kostas Blekos, Anastasios Tsakas, Christos Xouris, Ioannis Evdokidis, Dimitris Alexandropoulos, Christos Alexakos, Sofoklis Katakis, Andreas Makedonas, Christos Theoharatos and Aris Lalos
J. Sens. Actuator Netw. 2021, 10(1), 15; https://doi.org/10.3390/jsan10010015 - 18 Feb 2021
Cited by 13 | Viewed by 3988
Abstract
The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) [...] Read more.
The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) and the application of beneficial microorganisms (Micosat F BS WP) restore health in infected trees. However, for their efficient implementation the above methodologies require the marking of trees in the early stages of infestation—a task that is impractical with traditional means (manual labor) but also very difficult, as early stages are difficult to perceive with the naked eye. In this paper, we present the results of the My Olive Grove Coach (MyOGC) project, which used multispectral imaging from unmanned aerial vehicles to develop an olive grove monitoring system based on the autonomous and automatic processing of the multispectral images using computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, help in the prediction of Verticillium wilt spread and implement a decision support system that guides the farmer/agronomist. Full article
(This article belongs to the Special Issue Secure, Efficient Cyber-Physical Systems and Wireless Sensors)
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