Towards a Blockchain Database for Massive IoT Workloads

被引:4
作者
Drakatos, Panagiotis [1 ]
Demetriou, Erodotos [1 ]
Koumou, Stavroulla [1 ]
Konstantinidis, Andreas [2 ]
Zeinalipour-Yazti, Demetrios [1 ]
机构
[1] Univ Cyprus, Dept Comp Sci, CY-1678 Nicosia, Cyprus
[2] Frederick Univ, Dept Comp Sci, CY-1036 Nicosia, Cyprus
来源
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2021) | 2021年
关键词
blockchain; IoT; federated-learning; databases; INTERNET; CHALLENGES; THINGS;
D O I
10.1109/ICDEW53142.2021.00021
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The Internet of Things (IoT) revolution has massively 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 vision paper we propose Triabase, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. As such, it does not store detailed records on a medium, like blockchains, which is fundamentally very slow due to the expensive verification process. We lay out the primitive architectural blocks of our design, the requirements and the inherent challenges. Triabase employs technical novelties in respect to its consensus protocol, namely the notion of Proof-of-Federated-Learning (PoFL). The Triabase prototype system is implemented in the Hyperledger Fabric blockchain framework, upon which encouraging preliminary findings have been drawn.
引用
收藏
页码:76 / 79
页数:4
相关论文
共 25 条
[1]   Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications [J].
Al-Fuqaha, Ala ;
Guizani, Mohsen ;
Mohammadi, Mehdi ;
Aledhari, Mohammed ;
Ayyash, Moussa .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2015, 17 (04) :2347-2376
[2]   CAPER: A Cross-Application Permissioned Blockchain [J].
Amiri, Mohammad Javad ;
Agrawal, Divyakant ;
El Abbadi, Amr .
PROCEEDINGS OF THE VLDB ENDOWMENT, 2019, 12 (11) :1385-1398
[3]   Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains [J].
Androulaki, Elli ;
Barger, Artem ;
Bortnikov, Vita ;
Cachin, Christian ;
Christidis, Konstantinos ;
De Caro, Angelo ;
Enyeart, David ;
Ferris, Christopher ;
Laventman, Gennady ;
Manevich, Yacov ;
Muralidharan, Srinivasan ;
Murthy, Chet ;
Binh Nguyen ;
Sethi, Manish ;
Singh, Gari ;
Smith, Keith ;
Sorniotti, Alessandro ;
Stathakopoulou, Chrysoula ;
Vukolic, Marko ;
Cocco, Sharon Weed ;
Yellick, Jason .
EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, 2018,
[4]  
Atallah M., 2004, P 2004 ACM WORKSH PR, P103, DOI DOI 10.1145/1029179.1029204
[5]   The Internet of Things: A survey [J].
Atzori, Luigi ;
Iera, Antonio ;
Morabito, Giacomo .
COMPUTER NETWORKS, 2010, 54 (15) :2787-2805
[6]  
Billsus D., 1998, Machine Learning. Proceedings of the Fifteenth International Conference (ICML'98), P46
[7]  
Broder A., 2003, Internet Math., V1, P485, DOI DOI 10.1080/15427951.2004.10129096
[8]   A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective [J].
Chen, Shanzhi ;
Xu, Hui ;
Liu, Dake ;
Hu, Bo ;
Wang, Hucheng .
IEEE INTERNET OF THINGS JOURNAL, 2014, 1 (04) :349-359
[9]   Efficient Exploration of Telco Big Data with Compression and Decaying [J].
Costa, Constantinos ;
Chatzimilioudis, Georgios ;
Zeinalipour-Yazti, Demetrios ;
Mokbel, Mohamed F. .
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017), 2017, :1332-1343
[10]   Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond [J].
Dai, Yueyue ;
Xu, Du ;
Maharjan, Sabita ;
Chen, Zhuang ;
He, Qian ;
Zhang, Yan .
IEEE NETWORK, 2019, 33 (03) :10-17