Issues Related to the Use of Blockchains in IoT Applications

Vari-Kakas, Stefan; Poszet, Otto; Pater, Alexandrina Mirela; Moisi, Elisa Valentina; Vari-Kakas, Andor

doi:10.1109/emes52337.2021.9484103

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…We further used Linux MPSTAT, DSTAT, and Wireshark tools to monitor network bandwidth usage [33]. Additionally, we created python-based code to collect temperature data from IoT devices running blockchain applications and integrated it with the Linux "vcgencmd" tool [34]. In the next paragraphs, we discuss key hardware and software used to develop our test bed system.…”

Section: Research Methodology and Test Bed Developmentmentioning

confidence: 99%

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks

Arachchige,

Branch,

But

2024

Sensors

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The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.

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Section: Research Methodology and Test Bed Developmentmentioning

confidence: 99%

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks

Arachchige,

Branch,

But

2024

Sensors

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“…TLS requires a trusted certificate authority, which is contrary to the concept of blockchain decentralization. The low reliability of devices in the heterogeneous Internet of Things reduces the availability of threshold signatures, and it is difficult for threshold signatures to reach a consensus on frequently changing data [20].…”

Section: Cryptography-based Oraclementioning

confidence: 99%

A Caching-Enabled Permissioned Blockchain Framework for Industrial Internet of Things based on Deep Reinforcement Learning

Liu

Yao

et al. 2022

Preprint

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The integration of Industrial Internet of Things (IIoT) and blockchain has become a popular concept. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can provide great data processing capability and offer low-latency services. However, we are still facing the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage only allows them to store limited blockchain data. This work aims to improve the smart contract execution efficiency on these IIoT nodes by caching based on Deep Reinforcement Learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov Decision Process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experiment results show that our proposed framework can effectively reduce the block dissemination costs and the agent can provide satisfactory caching performance.

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Expert perspectives on blockchain in the circular economy: A Delphi study with industry specialists

Caldarelli

2024

Journal of Cleaner Production

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Issues Related to the Use of Blockchains in IoT Applications

Cited by 5 publications

References 19 publications

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks

A Caching-Enabled Permissioned Blockchain Framework for Industrial Internet of Things based on Deep Reinforcement Learning

Expert perspectives on blockchain in the circular economy: A Delphi study with industry specialists

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