A Blockchain-Based Decentralized Composition Solution for IoT Services

Ridhawi, Ismaeel Al; Aloqaily, Moayad; Boukerche, Azzedine; Jaraweh, Yaser

doi:10.1109/icc40277.2020.9149031

Cited by 33 publications

(12 citation statements)

References 15 publications

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“…Hence, our work, which aims to analyze transactions in a parallel and scalable way, can also be relevant to the analysis of massive IoT blockchain transactions. For example, the work [24] proposes a scalable decentralized service composition solution that uses a combination of blockchain, software defined networks and fog computing technologies.…”

Section: Previous Workmentioning

confidence: 99%

“…If the target node of the edge is a local node (line 19), and if the node is unvisited or it is visited but a shorter path is found, the node is added to the stack, the source node of the edge is set as its parent in the forest, and its depth is set as one added to the depth of the source node (lines 46-50). If the target node of the edge is a remote node (line 21), the source node and depth information is stored in a map S to be sent to the corresponding node in the communication part (lines [22][23][24][25][26]. This source node and depth information in the map is updated if a shorter path is found.…”

Section: Distributed Calculation Of the Blacklisted Address Trace Forestmentioning

confidence: 99%

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Parallel analysis of Ethereum blockchain transaction data using cluster computing

2022

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Ability to perform fast analysis on massive public blockchain transaction data is needed in various applications such as tracing fraudulent financial transactions. The blockchain data is continuously growing and is organized as a sequence of blocks containing transactions. This organization, however, cannot be used for parallel graph algorithms which need efficient distributed graph data structures. Using message passing libraries (MPI), we develop a scalable cluster-based system that constructs a distributed transaction graph in parallel and implement various transaction analysis algorithms. We report performance results from our system operating on roughly 5 years of 10.2 million block Ethereum Mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution, token transaction counting, connected components finding and our new parallel blacklisted address trace forest computation algorithm on a 16 node economical cluster set up on the Amazon cloud. Our system is able to construct a distributed graph of 766 million transactions in 218 s and compute the forest of blacklisted address traces in 32 s.

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Section: Previous Workmentioning

confidence: 99%

Section: Distributed Calculation Of the Blacklisted Address Trace Forestmentioning

confidence: 99%

Parallel analysis of Ethereum blockchain transaction data using cluster computing

2022

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“…Furthermore, a new proof of business consensus protocol was developed by Hu et al [89] to guarantee the incentive in blockchain-enabled federated slicing. Moreover, Ridhawi et al [90] studied the composition process in content delivery networks. Participants were rewarded by fog entities for solving this process in multimedia service delivery.…”

Section: Blockchain-enabled Incentive Mechanisms In Iotmentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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“…Some crowdsourcing models in blockchain environment combine apply auction mode to allocate tasks, [31] using repeated bidders bidding mechanism, deploying auction algorithm in blockchain environment, optimizing task allocation strategy, but not protecting user privacy, and only considering location factor in bidding selection criteria, ignoring other important attributes such as workers' reputation and compensation. [37] introduced a distributed service to deliver complex problems for cloud users. This scheme doesn't need any intermediary platform.…”

Section: B Task Allocation In Crowdsourcingmentioning

confidence: 99%

TSWCrowd: A Decentralized Task-Select-Worker Framework on Blockchain for Spatial Crowdsourcing

2020

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A Blockchain-Based Decentralized Composition Solution for IoT Services

Cited by 33 publications

References 15 publications

Parallel analysis of Ethereum blockchain transaction data using cluster computing

Parallel analysis of Ethereum blockchain transaction data using cluster computing

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

TSWCrowd: A Decentralized Task-Select-Worker Framework on Blockchain for Spatial Crowdsourcing

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