Blockchain-Based Security Deployment and Resource Allocation in SDN-Enabled MEC System

被引:0
作者
Zhao, Dongxiao
Zhang, Dawei [1 ,2 ,3 ]
Pei, Qingqi [1 ,2 ]
Liu, Lei [4 ]
Yue, Peixin [5 ]
机构
[1] Xidian Univ, Univ Shaanxi Prov, State Key Lab Integrated Service Networks, Sch Telecommun Engn, Xian 710071, Peoples R China
[2] Xidian Univ, Univ Shaanxi Prov, Engn Res Ctr Trusted Digital Econ, Xian 710071, Peoples R China
[3] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China
[4] Xidian Univ, Guangzhou Inst Technol, Guangzhou 510555, Peoples R China
[5] Agr Bank China, Res & Dev Dept, Xian 710018, Peoples R China
基金
中国国家自然科学基金;
关键词
Security; Blockchains; Network security; Firewalls (computing); Resource management; Optimization; Costs; Blockchain; mobile edge computing (MEC); resource allocation; security policy deployment; software defined network (SDN); FRAMEWORK; MANAGEMENT; NETWORKS;
D O I
10.1109/JIOT.2024.3455425
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The traditional data security systems have the problems, such as poor adaptability, technical barriers, and closed interfaces, which cannot meet the development requirements of beyond 5G (B5G) and the Internet of Things (IoT). In this article, we design a novel deployment network framework for adaptive security by using blockchain technology in the software defined network (SDN)-enabled mobile edge computing (MEC) system. The blockchain is deployed on multiple SDN servers, ensuring decision consistency and data security. The distributed SDN controllers can schedule and combine atomic security functions (ASFs) from the security resource pool of the MEC system to provide comprehensive security services. Furthermore, we developed a multiagent deep deterministic policy gradient (MADDPG) scheduling optimization algorithm to enhance the utility of our model while optimizing latency and energy cost. Simulations indicate that the algorithm successfully maximizes the overall utility of the MEC system, while adhering to the constraints of latency and energy cost.
引用
收藏
页码:40417 / 40430
页数:14
相关论文
共 34 条
[1]  
Chen J, 2021, ASIA-PAC CONF COMMUN, P89, DOI [10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00039, 10.1109/APCC49754.2021.9609866]
[2]   MEC-Assisted Immersive VR Video Streaming Over Terahertz Wireless Networks: A Deep Reinforcement Learning Approach [J].
Du, Jianbo ;
Yu, F. Richard ;
Lu, Guangyue ;
Wang, Junxuan ;
Jiang, Jing ;
Chu, Xiaoli .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (10) :9517-9529
[3]   QoE Fairness Resource Allocation in Digital Twin-Enabled Wireless Virtual Reality Systems [J].
Feng, Jie ;
Liu, Lei ;
Hou, Xiangwang ;
Pei, Qingqi ;
Wu, Celimuge .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2023, 41 (11) :3355-3368
[4]   Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks [J].
Feng, Jie ;
Liu, Lei ;
Pei, Qingqi ;
Li, Keqin .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2022, 33 (11) :2687-2700
[5]   Heterogeneous Computation and Resource Allocation for Wireless Powered Federated Edge Learning Systems [J].
Feng, Jie ;
Zhang, Wenjing ;
Pei, Qingqi ;
Wu, Jinsong ;
Lin, Xiaodong .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (05) :3220-3233
[6]   Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial [J].
Feriani, Amal ;
Hossain, Ekram .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2021, 23 (02) :1226-1252
[7]  
Haji SH, 2021, Asian Journal of Research in Computer Science, P1, DOI [10.9734/ajess/2021/v18i230441, 10.9734/ajrcos/2021/v9i230216, 10.9734/ajrcos/2021/v9i230216, DOI 10.9734/AJRCOS/2021/V9I230216]
[8]  
Hakiri A. H., 2019, P ACM INT WORKSH SOF, P11
[9]   P4-IPsec: Site-to-Site and Host-to-Site VPN With IPsec in P4-Based SDN [J].
Hauser, Frederik ;
Haeberle, Marco ;
Schmidt, Mark ;
Menth, Michael .
IEEE ACCESS, 2020, 8 :139567-139586
[10]  
Jeong ED, 2023, INT CONF NETW SER