Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks

Jianan Li, Jun Wu*, Jianhua Li, Ali Kashif Bashir, Md Jalil Piran, Ashiq Anjum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Collaborative inference helps robots to complete large tasks with mutual collaboration in edge-assisted multi-robot systems. It is challenging to provide trusted edge collaborative inference in the presence of malicious nodes. In this article, we propose a blockchain-based collaborative edge knowledge inference (BCEI) framework for edge-assisted multi-robot systems. First, we formulate the inference process at the edge as the collaborative knowledge graph construction and sharing model. Second, to guarantee the trust of knowledge sharing, an efficient knowledge-based blockchain consensus method is presented. Finally, we conduct a case study on the emergency rescue application to evaluate the proposed framework. The experiment results demonstrate the efficiency of the proposed framework in terms of latency and accuracy.

Original languageEnglish
Article number9502662
Pages (from-to)94-100
Number of pages7
JournalIEEE Communications Magazine
Volume59
Issue number7
DOIs
Publication statusPublished - 2021 Jul
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks'. Together they form a unique fingerprint.

Cite this