Integrating Data-Driven Security, Model Checking, and Self-adaptation for IoT Systems Using BIP Components: A Conceptual Proposal Model

AlSobeh, Anas M. R.; Shatnawi, Amani

doi:10.1007/978-3-031-33743-7_44

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…During the wine production stage, ML algorithms facilitate production planning in order to ensure a high-quality wine output. Predictive algorithms help optimize fermentation processes, monitor grape ripeness, and manage fermentation temperatures to maintain wine quality and consistency [57,58].…”

Section: Ai In the Wine Supply Chainmentioning

confidence: 99%

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

Adamashvili,

Zhizhilashvili,

Tricase

2024

Computers

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The study presents a comprehensive examination of the recent advancements in the field of wine production using the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain Technology (BCT). The paper aims to provide insights into the implementation of these technologies in the wine supply chain and to identify the potential benefits associated with their use. The study highlights the various applications of IoT, AI, and BCT in wine production, including vineyard management, wine quality control, and supply chain management. It also discusses the potential benefits of these technologies, such as improved efficiency, increased transparency, and reduced costs. The study concludes by presenting the framework proposed by the authors in order to overcome the challenges associated with the implementation of these technologies in the wine supply chain and suggests areas for future research. The proposed framework meets the challenges of lack of transparency, lack of ecosystem management in the wine industry and irresponsible spending associated with the lack of monitoring and prediction tools. Overall, the study provides valuable insights into the potential of IoT, AI, and BCT in optimizing the wine supply chain and offers a comprehensive review of the existing literature on the study subject.

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Section: Ai In the Wine Supply Chainmentioning

confidence: 99%

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

Adamashvili,

Zhizhilashvili,

Tricase

2024

Computers

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“…This underscores the need for comprehensive benchmarking frameworks that can systematically evaluate the real-world performance of LCCD models, uncover their strengths and limitations, and guide the selection of suitable approaches tailored to specific application requirements and operational constraints. Such frameworks are crucial for driving future research towards developing more accurate, robust, and versatile CD solutions aligned with practical needs [62].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Generic and Extendable Framework for Benchmarking and Assessing the Change Detection Models

Hassouna,

Ismail,

Alqahtani

et al. 2024

Preprint

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Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

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“…Through the analysis of the formal model under different scenarios and property specifications, model checking aids in identifying trade-offs, potential bottlenecks, or performance concerns, enabling the optimization of the system's design to enhance correctness and reliability [6]. However, conventional model-checking approaches face challenges in handling the inherent dynamism of blockchain systems that can arise from runtime changes or developing requirements [7]. To overcome these limitations, innovative model-checking methods are needed that can capture and analyze the dynamic behaviors exhibited by blockchain systems [8].…”

Section: Introductionmentioning

confidence: 99%

BlockASP: A Framework for AOP-Based Model Checking Blockchain System

AlSobeh,

Magableh

2023

IEEE Access

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Blockchain systems are lauded for their security and reliability. Security is a cornerstone, as they employ cryptographic techniques to ensure the immutability of data, making it extremely resistant to tampering. With decentralized networks, they also reduce the risk of a single point of failure, enhancing reliability. Model checking plays a vital role in ensuring the security and reliability of blockchain systems. However, traditional model-checking approaches face challenges in handling the inherent dynamism exhibited in blockchain systems. To overcome this challenge, Aspect-Oriented programming (AOP) offers capabilities to enhance blockchain model checking through the modularization of cross-cutting concerns, enabling traceability and monitoring, facilitating dynamic instrumentation, and supporting fine-grained property specifications. The aim of this research is to enable more effective and efficient verification of dynamic behaviors in blockchain systems compared to conventional model-checking techniques using AOP. As a result, this research introduces BlockASP, a novel blockchain model verification method that leverages AOP to analyze and monitor dynamic behavior of the blockchain system. BlockASP integrates the benefits of aspect-orientation and model checking into the blockchain architecture to strengthen security and reliability. This research has examined prior arts that are related to blockchain modeling using Objectoriented (OO) and those that are using AOP. Our research has proposed and discussed the BlockASP technique, the research provided a case study to demonstrate the validity and superiority in facilitating the monitoring of dynamic blockchain behavior using AOP compared to traditional approaches such as Model-Driven Architecture (MDA).

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Integrating Data-Driven Security, Model Checking, and Self-adaptation for IoT Systems Using BIP Components: A Conceptual Proposal Model

Cited by 15 publications

References 19 publications

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

A Generic and Extendable Framework for Benchmarking and Assessing the Change Detection Models

BlockASP: A Framework for AOP-Based Model Checking Blockchain System

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