Internet of Things Security: Mathematical Perspective

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 791

Special Issue Editor


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Guest Editor
School of Information Technology, Deakin University, Geelong, VIC 3216, Australia
Interests: authentication; blockchain; IoT; post-quantum; fog/edge computing

Special Issue Information

Dear Colleagues,

The intersection of the Internet of Things (IoT) and cybersecurity presents both notable challenges and promising opportunities. IoT devices, renowned for their versatile functionalities and resource limitations, play pivotal roles across diverse domains such as Smart Homes, Smart Cities, and the Industrial Internet. However, these devices are vulnerable to a range of security threats, underscoring the critical need for robust security solutions and protocols.

This special issue aims to gather contributions from leading-edge researchers and industry experts to address critical issues in IoT security from a mathematical perspective. To achieve this goal, our focus will be on, but not limited to, the following topics:

  • Designing and Validating Security Protocols for IoT Environments
  • Developing Formal/Mathematical Proofs for Security Protocols in IoT
  • Developing Automated Software Verification for IoT Security Protocols
  • Designing and Implementing Quantum-Proof Security Protocols for IoT
  • Addressing Challenges and Implementing Solutions: Decentralized (Blockchain-based) Security for IoT
  • Designing suitable cryptosystems for IoT and verifying their hard problems
  • Designing privacy-preserving and anonymous protocols for IoT using creative mathematical-based approaches

Dr. Ali Shahidinejad
Guest Editor

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Keywords

  • Internet of Things (IoT)
  • security protocol design
  • formal proof
  • automated software verification
  • cryptography
  • post-quantum cryptography
  • blockchain

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Published Papers (1 paper)

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20 pages, 4811 KiB  
Article
Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications
by Abdullah Alghuried, Moahd Khaled Alghuson, Turki S. Alahmari and Khaled Ali Abuhasel
Mathematics 2024, 12(15), 2385; https://doi.org/10.3390/math12152385 - 31 Jul 2024
Viewed by 628
Abstract
Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, [...] Read more.
Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag. Full article
(This article belongs to the Special Issue Internet of Things Security: Mathematical Perspective)
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