Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism

By Junqin Huang, L. Kong, Guihai Chen, Minyou Wu, Xuemei Liu, Peng Zeng. IEEE Transactions on Industrial Informatics, 2019

440 citations recorded by the source corpus.

Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/9be319953ae3b40f7c9fe70f649c1ff60915e6f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

  1. Michele De Donno
  2. Neeraj Kumar
  3. Sajal K. Das
  4. Sudhanshu Tyagi
  5. Junqin Huang
  6. Nicola Dragoni
  7. L. Kong
  8. Sudeep Tanwar

In Information Systems

  1. Patricia Baudier
  2. Michele De Donno
  3. Umesh Bodkhe
  4. Neeraj Kumar
  5. Xenofon Fafoutis
  6. Karan Parekh
  7. Qianwen Ariel Xu
  8. Victor I. Chang

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