A Survey on Blockchain-Based Trust Management for Internet of Things

Liu, Yijia; Wang, Jie; Yan, Zheng; Wan, Zhiguo; Jäntti, Riku

doi:10.1109/jiot.2023.3237893

Cited by 63 publications

(16 citation statements)

References 152 publications

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“…The IoB turns into a tool for accurate prediction when it has particular users [54]. This works differently from the other apps that look to follow human motions, explore their locations, detect their gestures, and specify their vicinity to one another [55]. However, integrating these methods can generate a very strong and smart service at the same time [56].…”

Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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“…The fast development of AI technologies like deep learning is a promising approach for extracting accurate information from massive raw sensor data in IoT applications [3]. In addition, due to its tamper-proof characteristic and distributed nature, blockchain has received increasing attentions in emerging IoT applications to tackle security and privacy issues [4], [5]. AI and blockchain have become killer technologies to advance the fast development of IoT ecosystems with incredible growth, impact, and potential.…”

Section: Guest Editorial Special Issue On Ai and Blockchain-poweredmentioning

confidence: 99%

Guest Editorial Special Issue on AI and Blockchain-Powered IoT Sustainable Computing

Zhang

Yan

et al. 2023

IEEE Internet Things J.

Self Cite

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“…Therefore, the ability to construct contracts should possess the flexibility to adapt to changes in the entire data flow across devices, particularly when leveraging decentralized blockchain technology for monitoring purposes. 1 The design of a solution to solve any sort of data transmission-related issues can vary based on the critical nature of the application under consideration. For example, the information generated from social media that is publicly visible to all needs to be processed at a less critical rate than that of banking, financial, and various other mission-critical applications.…”

Section: Introductionmentioning

confidence: 99%

“…Even with multiple levels of automation and integration, ensuring the effectiveness of sensors represents an added cost. Therefore, the ability to construct contracts should possess the flexibility to adapt to changes in the entire data flow across devices, particularly when leveraging decentralized blockchain technology for monitoring purposes 1 …”

Section: Introductionmentioning

confidence: 99%

Secure data access using blockchain technology through IoT cloud and fabric environment

Gupta,

Chithaluru,

El Barachi

et al. 2023

Security and Privacy

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In the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.

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A Survey on Blockchain-Based Trust Management for Internet of Things

Cited by 63 publications

References 152 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Guest Editorial Special Issue on AI and Blockchain-Powered IoT Sustainable Computing

Secure data access using blockchain technology through IoT cloud and fabric environment

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