Applied Sciences

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14 pages, 7901 KiB

Open AccessArticle

Secure and Transparent Craftwork Authentication and Transaction System: Integrating Digital Fingerprinting and Blockchain Technologies

by Ji Hyun Yi and Jinsoo Moon

Appl. Sci. 2024, 14(19), 9054; https://doi.org/10.3390/app14199054 - 7 Oct 2024

Viewed by 691

Abstract

This study proposes a method that enables craftsmen to define and apply the unique characteristics of their craftworks to distinguish between originals and imitations and to protect and trade their intellectual property rights. In the first step, a digital fingerprint that enables the [...] Read more.

This study proposes a method that enables craftsmen to define and apply the unique characteristics of their craftworks to distinguish between originals and imitations and to protect and trade their intellectual property rights. In the first step, a digital fingerprint that enables the authentication of the original craftworks was generated by applying hash functions that can digitize various attributes of the craftworks and create a unique ID. In the second step, a blockchain transaction system for the original authentication of the craftwork was developed by applying consortium blockchain technology. This system allows multiple craft-related organizations to participate together, and when a transaction occurs, a smart contract is created and stored on the blockchain, thereby enabling the tracking and management of transaction histories. Furthermore, a DApp was developed that enables buyers to verify the craftwork authentication and access detailed information by scanning the digital fingerprint (QR code) of the craftwork, which is integrated with the blockchain system. In the third step, the research results were evaluated through a satisfaction survey conducted with 121 participants and a usability evaluation with 10 craftsmen, both of which yielded positive feedback. This study successfully realizes a secure and transparent craftwork transaction system that guarantees both security and efficiency through the integration of digital fingerprinting and blockchain technologies. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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14 pages, 1345 KiB

Open AccessArticle

Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques

by Alicia Pedrosa-Rodriguez, Carmen Camara and Pedro Peris-Lopez

Appl. Sci. 2024, 14(19), 8945; https://doi.org/10.3390/app14198945 - 4 Oct 2024

Viewed by 761

Abstract

Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1–2% of the global population, requires accurate detection methods due to its [...] Read more.

Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1–2% of the global population, requires accurate detection methods due to its prevalence and health impact. This study employs IoT devices to capture PPG signals and implements comprehensive preprocessing steps, including windowing, filtering, and artifact removal, to extract relevant features for classification. We explored a broad range of machine learning (ML) and deep learning (DL) approaches. Our results demonstrate superior performance, achieving an accuracy of 97.7%, surpassing state-of-the-art methods, including those with FDA clearance. Key strengths of our proposal include the use of shortened 15-second traces and validation using publicly available datasets. This research advances the design of cost-effective IoT devices for AF detection by leveraging diverse ML and DL techniques to enhance classification accuracy and robustness. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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25 pages, 1019 KiB

Open AccessArticle

Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities

by Dulana Rupanetti and Naima Kaabouch

Appl. Sci. 2024, 14(16), 7104; https://doi.org/10.3390/app14167104 - 13 Aug 2024

Cited by 2 | Viewed by 2697

Abstract

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published [...] Read more.

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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24 pages, 4686 KiB

Open AccessArticle

User-Centric Internet of Things and Controlled Service Scheduling Scheme for a Software-Defined Network

by Mohd Anjum, Hong Min and Zubair Ahmed

Appl. Sci. 2024, 14(11), 4951; https://doi.org/10.3390/app14114951 - 6 Jun 2024

Cited by 2 | Viewed by 916

Abstract

Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a [...] Read more.

Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a major issue for SDN-based IoT applications. This paper uses the Controlled Service Scheduling Scheme (CS3) to address the challenge of simultaneous scheduling and power allocation. The suggested approach uses deep recurrent learning and probabilistic balancing for power allocation and service distribution during user-centric concurrent sharing intervals. The SDN control plane decides how much power to use for service delivery while forecasting user service demands directs the scheduling interval allocation. Power management is under the control plane of the SDN, whereas service distribution is under the data plane. Power-to-service requirements are evaluated probabilistically, and updates for both aircraft are obtained via the deep learning model. This allocation serves as the basis for training the learning model to alleviate power deficits across succeeding intervals. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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20 pages, 6246 KiB

Open AccessArticle

A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference

by Meng Yu, Shanglei Zhu, Bao Lu, Qiang Chen and Tengfei Wang

Appl. Sci. 2024, 14(11), 4779; https://doi.org/10.3390/app14114779 - 31 May 2024

Viewed by 697

Abstract

Automatic Container Code Recognition (ACCR) is critical for enhancing the efficiency of container terminals. However, existing ACCR methods frequently fail to achieve satisfactory performance in complex environments at port gates. In this paper, we propose an approach for accurate, fast, and compact container [...] Read more.

Automatic Container Code Recognition (ACCR) is critical for enhancing the efficiency of container terminals. However, existing ACCR methods frequently fail to achieve satisfactory performance in complex environments at port gates. In this paper, we propose an approach for accurate, fast, and compact container code recognition by utilizing YOLOv4 for container region localization and Deeplabv3+ for character recognition. To enhance the recognition speed and accuracy of YOLOv4 and Deeplabv3+, and to facilitate their deployment at gate entrances, we introduce several improvements. First, we optimize the feature-extraction process of YOLOv4 and Deeplabv3+ to reduce their computational complexity. Second, we enhance the multi-scale recognition and loss functions of YOLOv4 to improve the accuracy and speed of container region localization. Furthermore, we adjust the dilated convolution rates of the ASPP module in Deeplabv3+. Finally, we replace two upsampling structures in the decoder of Deeplabv3+ with transposed convolution upsampling and sub-pixel convolution upsampling. Experimental results on our custom dataset demonstrate that our proposed method, C-YOLOv4, achieves a container region localization accuracy of 99.76% at a speed of 56.7 frames per second (FPS), while C-Deeplabv3+ achieves an average pixel classification accuracy (MPA) of 99.88% and an FPS of 11.4. The overall recognition success rate and recognition speed of our approach are 99.51% and 2.3 ms per frame, respectively. Moreover, C-YOLOv4 and C-Deeplabv3+ outperform existing methods in complex scenarios. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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22 pages, 1264 KiB

Open AccessArticle

FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication

by Mona Alkanhal, Mohamed Younis, Abdulaziz Alali and Suhee Sanjana Mehjabin

Appl. Sci. 2024, 14(8), 3363; https://doi.org/10.3390/app14083363 - 16 Apr 2024

Viewed by 1026

Abstract

The Internet of Things (IoT) reflects the internetworking of numerous devices with limited computational capabilities. Given the ad-hoc network formation and the dynamic nature of node membership, secure device authentication mechanisms are critical. This paper proposes a novel two-factor authentication protocol for IoT [...] Read more.

The Internet of Things (IoT) reflects the internetworking of numerous devices with limited computational capabilities. Given the ad-hoc network formation and the dynamic nature of node membership, secure device authentication mechanisms are critical. This paper proposes a novel two-factor authentication protocol for IoT devices. The protocol integrates physical unclonable functions (PUFs) and radio frequency fingerprints (RFFs), providing a unique identification method for each device. Compared with existing PUF-based schemes, the proposed protocol facilitates the mutual authentication of two devices without the need for a trusted third party. Our design is resilient to the intrinsic noise associated with PUFs and RFFs, ensuring reliable authentication, even under various operational conditions. Furthermore, we have implemented an obfuscation technique to secure shared authentication data against eavesdropping attempts aimed at modeling the security primitive, i.e., the PUF, through machine learning algorithms. We have validated the performance of our protocol and demonstrated its efficacy against various security threats, including impersonation, message replay, and PUF modeling attacks. Notably, the validation results indicate that predicting any given PUF response bit’s accuracy does not exceed 56%, making it as unpredictable as a random guess. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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23 pages, 2428 KiB

Open AccessArticle

Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration

by David Sarabia-Jácome, Sergio Giménez-Antón, Athanasios Liatifis, Eduard Grasa, Marisa Catalán and Dimitrios Pliatsios

Appl. Sci. 2024, 14(6), 2300; https://doi.org/10.3390/app14062300 - 9 Mar 2024

Cited by 2 | Viewed by 1144

Abstract

Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In [...] Read more.

Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In view of this, IoT networks require innovative network architectures that offer scalability, security, and adaptability. The Recursive InterNetwork Architecture (RINA) is a clean slate network architecture that provides a scalable, secure, and flexible framework for interconnecting computers. SDN technology is becoming a de facto solution to overcome network requirements, making RINA adoption difficult. This paper presents an architecture for integrating RINA with SDN technologies to lower the barriers of adopting RINA in IoT environments. The architecture relies on a RINA-based distributed application facility (DAF), a RINA southbound driver (SBI), and the RINA L2VPN. The RINA-based DAF manages RINA nodes along the edge–fog–cloud continuum. The SBI driver SDN enables the hybrid centralized management of SDN switches and RINA nodes. Meanwhile, the RINA L2VPN allows seamless communication between edge nodes and the cloud to facilitate the data exchange between network functions (NFs). Such integration has enabled a progressive deployment of RINA in current IoT networks without affecting their operations and performance. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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24 pages, 1730 KiB

Open AccessArticle

An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS

by Abuzar Zafar, Fahad Samad, Hassan Jamil Syed, Ashraf Osman Ibrahim, Manar Alohaly and Muna Elsadig

Appl. Sci. 2023, 13(13), 7856; https://doi.org/10.3390/app13137856 - 4 Jul 2023

Cited by 2 | Viewed by 2954

Abstract

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT [...] Read more.

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics. Full article

(This article belongs to the Special Issue Next-Generation of Internet of Things (IoT): New Advances, Solutions, Applications, Services and Challenges)

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