IEICE TRANSACTIONS on Communications
Edge Computing Resource Allocation Algorithm for NB-IoT Based on Deep Reinforcement Learning
Summary :
Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.5 pp.439-447
- Publication Date
- 2023/05/01
- Publicized
- 2022/11/04
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022EBP3076
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Jiawen CHU
Beijing Information Science and Technology University
Chunyun PAN
Beijing Information Science and Technology University
Yafei WANG
Beijing Information Science and Technology University
Xiang YUN
Baicells Technologies Co., Ltd.
Xuehua LI
Beijing Information Science and Technology University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Jiawen CHU, Chunyun PAN, Yafei WANG, Xiang YUN, Xuehua LI, "Edge Computing Resource Allocation Algorithm for NB-IoT Based on Deep Reinforcement Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 5, pp. 439-447, May 2023, doi: 10.1587/transcom.2022EBP3076.
Abstract: Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3076/_p
Copy
@ARTICLE{e106-b_5_439,
author={Jiawen CHU, Chunyun PAN, Yafei WANG, Xiang YUN, Xuehua LI, },
journal={IEICE TRANSACTIONS on Communications},
title={Edge Computing Resource Allocation Algorithm for NB-IoT Based on Deep Reinforcement Learning},
year={2023},
volume={E106-B},
number={5},
pages={439-447},
abstract={Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.},
keywords={},
doi={10.1587/transcom.2022EBP3076},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - Edge Computing Resource Allocation Algorithm for NB-IoT Based on Deep Reinforcement Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 439
EP - 447
AU - Jiawen CHU
AU - Chunyun PAN
AU - Yafei WANG
AU - Xiang YUN
AU - Xuehua LI
PY - 2023
DO - 10.1587/transcom.2022EBP3076
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2023
AB - Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.
ER -
English
