TY - JOUR
T1 - Cooperative Task Offloading and Block Mining in Blockchain-Based Edge Computing With Multi-Agent 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 - Funding Information:
This work was supported in part by CSIRO Data61, Australia, and in part by the U.S. National Science Foundation under Grant CCF-1908308. The work of Jun Li was supported by the National Natural Science Foundation of China under Grant 61872184
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered 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 for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.
AB - The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered 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 for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.
KW - Blockchain
KW - block mining
KW - deep reinforcement learning
KW - mobile edge computing
KW - task offloading
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U2 - 10.1109/TMC.2021.3120050
DO - 10.1109/TMC.2021.3120050
M3 - Article
AN - SCOPUS:85117750721
SN - 1536-1233
VL - 22
SP - 2021
EP - 2037
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 4
ER -