Abstract
AbstractIn the era of the 5G network, network traffic grows rapidly. Edge computing will face the challenges of high bandwidth, low latency, high reliability and other requirements of 5G network services. Due to the limited resources of node communication, computing and storage, under the sudden, intensive and high-traffic task request, edge computing will suffer from network jitter, excessive delay, access congestion, service failure and low distribution efficiency. In order to ensure network service quality and improve service efficiency, it is necessary to construct an effective collaborative service mechanism, motivate nodes to participate in cooperation, integrate network service resources and self-adapt to manage collaborative services. Therefore, establishing an effective node and service evaluation system to identify reliable resources and nodes is an effective way to improve the overall availability and reliable services of edge computing network. In this paper, we summarize and analyze the key technologies of the current collaborative service organization incentive and trust mechanism. This paper presents an attack-resistant node and service evaluation system based on a trust network. The system includes a voting collection mechanism, a trustable node selection mechanism, a questioning response mechanism and a punishment mechanism. The experiments prove that the system has a strong ability to resist attacks and is superior to the existing reputation evaluation model in terms of its performance. It can effectively improve the collaborative efficiency of edge computing and guarantee the quality of network service.
Funder
National Natural Science Foundation of China
National Key R&D Program of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Computer Science Applications,Signal Processing
Reference28 articles.
1. V. Turner, J.F. Gantz, D. Reinsel, et al, The digital universe of opportunities: rich data and the increasing value of the Internet of things. https://www.emc.com/leadership/digital-universe/2014iview/highvalue-data.htm
2. Cisco. Cisco Visual, Networking Index: Global Mobile Data Traffic Forecast 2016–2021 Q&A. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-forecast-qa.html.
3. M. Shafiq, Z. Tian, A.K. Bashir, X. Du, M. Guizani, CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine learning techniques. Internet Things J. PP(99), 1 (2020). https://doi.org/10.1109/JIOT.2020.3002255
4. J. Qiu, Z. Tian, C. Du, Q. Zuo, S. Su, B. Fang, A survey on access control in the age of internet of things. IEEE Internet Things J. 7(6), 4682–4696 (2020)
5. N. Hu, Z. Tian, H. Lu, X. Du, M. Guizani, A multiple-kernel clustering based intrusion detection scheme for 5g and iot networks. Int. J. Mach. Learn. Cybern. (2021). https://doi.org/10.1007/s13042-020-01253-w
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献