TY - JOUR
T1 - Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks
AU - Li, Jianan
AU - Wu, Jun
AU - Li, Jianhua
AU - Bashir, Ali Kashif
AU - Piran, Md Jalil
AU - Anjum, Ashiq
N1 - Funding Information:
Acknowledgment This work was supported in part by the National Natural Science Foundation of China under Grant No. 61972255 and U20B2048.
Publisher Copyright:
© 1979-2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
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U2 - 10.1109/MCOM.001.2000419
DO - 10.1109/MCOM.001.2000419
M3 - Article
AN - SCOPUS:85111755400
SN - 0163-6804
VL - 59
SP - 94
EP - 100
JO - IEEE Communications Society Magazine
JF - IEEE Communications Society Magazine
IS - 7
M1 - 9502662
ER -