Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing

被引:87
|
作者
Xu, Xiaolong [1 ,2 ,3 ]
Liu, Xihua [4 ]
Xu, Zhanyang [4 ]
Dai, Fei [5 ]
Zhang, Xuyun [6 ]
Qi, Lianyong [7 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Jiangsu Engn Ctr Network Monitoring, Nanjing 210044, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China
[3] Minist Educ, Engn Res Ctr Digital Forens, Nanjing 210044, Peoples R China
[4] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China
[5] Southwest Forestry Univ, Sch Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China
[6] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia
[7] Qufu Normal Univ, Sch Informat Sci & Engn, Qufu 273165, Shandong, Peoples R China
来源
IEEE INTERNET OF THINGS JOURNAL | 2020年 / 7卷 / 05期
基金
中国国家自然科学基金;
关键词
Edge computing; Internet of Things (IoT); privacy leakage; service placement; smart cities; MOBILE-EDGE; BIG DATA; PRIVACY; CLOUD; MANAGEMENT; SECURITY;
D O I
10.1109/JIOT.2019.2959124
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.
引用
收藏
页码:4084 / 4091
页数:8
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