TY - GEN
T1 - Utility Optimization for Blockchain Empowered Edge Computing with Deep Reinforcement Learning
AU - Nguyen, Dinh C.
AU - Ding, Ming
AU - Pathirana, Pubudu N.
AU - Seneviratne, Aruna
AU - Li, Jun
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.
AB - The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.
UR - http://www.scopus.com/inward/record.url?scp=85115704386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115704386&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500648
DO - 10.1109/ICC42927.2021.9500648
M3 - Conference contribution
AN - SCOPUS:85115704386
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -