TY - JOUR
T1 - UAV-Assisted Computation Offloading Toward Energy-Efficient Blockchain Operations in Internet of Things
AU - Lan, Xunqiang
AU - Tang, Xiao
AU - Zhang, Ruonan
AU - Lin, Wensheng
AU - Han, Zhu
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - The advances in the Internet of Things (IoT) have provided generous opportunities for various applications, yet the data security concerns need to be specially addressed due to the limited capability of IoT devices. Toward this issue, we propose to deploy a blockchain system with practical Byzantine fault tolerance (PBFT) consensus for IoT data security provisioning. In particular, we conduct profound analyses regarding the PBFT procedure and elaborate on the computation burden therein. Then, an unmanned aerial vehicle (UAV) is leveraged to offload the computation and assist the blockchain operations. The system energy efficiency is introduced as the ratio of the blockchained IoT data amount and energy consumption for the computation and communication in the network. Further, we maximize the system energy efficiency by the joint design of offloading and transmission strategies, as well as the UAV deployment, within a reinforcement learning framework. Finally, simulation results are provided to corroborate the effectiveness of our proposal.
AB - The advances in the Internet of Things (IoT) have provided generous opportunities for various applications, yet the data security concerns need to be specially addressed due to the limited capability of IoT devices. Toward this issue, we propose to deploy a blockchain system with practical Byzantine fault tolerance (PBFT) consensus for IoT data security provisioning. In particular, we conduct profound analyses regarding the PBFT procedure and elaborate on the computation burden therein. Then, an unmanned aerial vehicle (UAV) is leveraged to offload the computation and assist the blockchain operations. The system energy efficiency is introduced as the ratio of the blockchained IoT data amount and energy consumption for the computation and communication in the network. Further, we maximize the system energy efficiency by the joint design of offloading and transmission strategies, as well as the UAV deployment, within a reinforcement learning framework. Finally, simulation results are provided to corroborate the effectiveness of our proposal.
KW - Internet of Things
KW - blockchain
KW - computation offloading
KW - deep reinforcement learning
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85161055741&partnerID=8YFLogxK
U2 - 10.1109/LWC.2023.3279317
DO - 10.1109/LWC.2023.3279317
M3 - 文章
AN - SCOPUS:85161055741
SN - 2162-2337
VL - 12
SP - 1469
EP - 1473
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 8
ER -
