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Artificial Intelligence for Security and Privacy in Ad Hoc and Sensor Networks (AI-SPASN)

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 2989
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

Special Issue Editors


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Guest Editor
Western Michigan University, Kalamazoo, Michigan, USA
Interests: trust, privacy, and security in open computing systems; ad hoc and opportunistic computing systems; software system architecture

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Guest Editor
Qualcomm Inc., San Diego, California, USA
Interests: machine learning; deep learning; cybersecurity; networks; Internet of Things

Special Issue Information

Dear Colleagues,

The world—both living and inanimate—is becoming more and more interconnected via a complex web of networks and sensors, collecting rapidly growing volumes of increasingly complex multimodal data and serving wider varieties of smart entities—from smartwatches to smart cities.

The immense scope and value of collected data provides motivation for attackers to invest in vastly more capable hardware and software—the latter including AI-based means of attack. The exponential increase in attack capabilities calls for countering them with the smartest software that can be built: AI-based controls.

These AI-based defenses must be deployed to increase the security and privacy of users, networks, applications, and data by eliminating or at least reducing vulnerabilities, recognizing threats, and preventing attacks or at least detecting them at the earliest available opportunity.

This Special Issue “Artificial Intelligence for Security and Privacy in Ad Hoc and Sensor Networks” (AI-SPASN) concentrates on new methodologies, techniques, and tools for identifying vulnerabilities and threats to the security and privacy of ad hoc and sensor networks and countering attacks on them while using the power of AI.

AI-SPASN invites high-quality contributions detailing novel, significant, and otherwise unpublished results. Solicited topics are limited to applications of Artificial Intelligence for improving security and privacy in ad hoc and sensor networks. The topics include, but are not limited to the following subareas and topics:

  • AI-based methodologies improving security and privacy in ad hoc and sensor networks:
    1. Machine learning, adversarial machine learning, deep learning, and automated reasoning
    2. Privacy-preserving methodologies for networks, applications, and data
    3. Predictive network modeling
    4. Blockchain-based approaches
    5. Human–computer interactions
  • AI-based techniques and tools improving security and privacy in ad hoc and sensor networks:
    1. Code and data obfuscation
    2. Prevention of network data leakage
    3. Network activity analysis and visualization
    4. Communication protocols
    5. Network data processing
  • Application areas for ad hoc and sensor networks with AI-based security and privacy controls:
    1. Autonomous vehicles
    2. Biosensors
    3. Healthcare systems
    4. Internet of Things
    5. MANETs
    6. Opportunistic systems
    7. Space systems
    8. Transportation systems

Prof. Leszek T. Lilien
Dr. Ganapathy Mani
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

  • adversarial machine learning
  • autonomous vehicles
  • biosensor networks
  • blockchain
  • cybersecurity
  • deep learning
  • human–computer interaction
  • Internet of Things
  • machine learning
  • MANETs
  • privacy
  • opportunistic networks
  • sensor networks
  • smart sensors
  • space networks
  • transportation networks

Published Papers (1 paper)

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Research

18 pages, 1916 KiB  
Article
Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain
by Yoon-Su Jeong
Sensors 2022, 22(12), 4645; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124645 - 20 Jun 2022
Cited by 7 | Viewed by 1685
Abstract
Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing [...] Read more.
Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average. Full article
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