A blockchain datastore for scalable IoT workloads using data decaying

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Publicado en:Distributed and Parallel Databases vol. 42, no. 3 (Sep 2024), p. 403
Autor principal: Drakatos, Panagiotis
Otros Autores: Costa, Constantinos, Konstantinidis, Andreas, Chrysanthis, Panos K., Zeinalipour-Yazti, Demetrios
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Springer Nature B.V.
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Acceso en línea:Citation/Abstract
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100 1 |a Drakatos, Panagiotis  |u University of Cyprus, Department of Computer Science, Nicosia, Cyprus (GRID:grid.6603.3) (ISNI:0000 0001 2116 7908) 
245 1 |a A blockchain datastore for scalable IoT workloads using data decaying 
260 |b Springer Nature B.V.  |c Sep 2024 
513 |a Journal Article 
520 3 |a The Internet of Things (IoT) revolution has introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this paper we introduce Triabase, a novel permissioned blockchain system architecture that applies data decaying concepts to cope with scalability issues in regards to blockchain consensus and storage efficiency. For blockchain consensus, we propose the Proof of Federated Learning (PoFL) algorithm which exploits data decaying models as Proof-of-Work. For storage efficiency, we exploit federated learning to construct data postdiction machine learning models to minimize the storage of bulky data on the blockchain. We present a detailed explanation of our system architecture as well as the implementation in the Hyperledger fabric framework. We use our implementation to carry out an experimental evaluation with telco big data at scale showing that our framework exposes desirable qualities, namely efficient consensus at the blockchain layer while optimizing storage efficiency. 
653 |a Big Data 
653 |a Machine learning 
653 |a Collaboration 
653 |a Internet of Things 
653 |a Computer architecture 
653 |a Artificial intelligence 
653 |a Databases 
653 |a Blockchain 
653 |a Efficiency 
653 |a Digital currencies 
653 |a Semantic web 
653 |a Queries 
653 |a Federated learning 
700 1 |a Costa, Constantinos  |u University of Cyprus, Department of Computer Science, Nicosia, Cyprus (GRID:grid.6603.3) (ISNI:0000 0001 2116 7908); University of Pittsburgh, Department of Computer Science, Pittsburgh, USA (GRID:grid.21925.3d) (ISNI:0000 0004 1936 9000) 
700 1 |a Konstantinidis, Andreas  |u University of Cyprus, Department of Computer Science, Nicosia, Cyprus (GRID:grid.6603.3) (ISNI:0000 0001 2116 7908); Frederick University, Department of Computer Science, Nicosia, Cyprus (GRID:grid.434490.e) (ISNI:0000 0004 0478 4359) 
700 1 |a Chrysanthis, Panos K.  |u University of Cyprus, Department of Computer Science, Nicosia, Cyprus (GRID:grid.6603.3) (ISNI:0000 0001 2116 7908); University of Pittsburgh, Department of Computer Science, Pittsburgh, USA (GRID:grid.21925.3d) (ISNI:0000 0004 1936 9000) 
700 1 |a Zeinalipour-Yazti, Demetrios  |u University of Cyprus, Department of Computer Science, Nicosia, Cyprus (GRID:grid.6603.3) (ISNI:0000 0001 2116 7908) 
773 0 |t Distributed and Parallel Databases  |g vol. 42, no. 3 (Sep 2024), p. 403 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3255420266/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
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