Trust management for internet of things using cloud computing and security in smart cities

被引:0
作者
Mamoun Alazab
Gunasekaran Manogaran
Carlos Enrique Montenegro-Marin
机构
[1] College of Engineering,Department of Electrical Engineering & Computer Science
[2] IT and Environment at Charles Darwin University,College of Information and Electrical Engineering
[3] Howard University,Facultad de Ingeniería
[4] Asia University,undefined
[5] Universidad Distrital Francisco José de Caldas,undefined
来源
Cluster Computing | 2022年 / 25卷
关键词
Smart city; Internet of things; Security; Communication;
D O I
暂无
中图分类号
学科分类号
摘要
Many consumers participate in the smart city via smart portable gadgets such as wearables, personal gadgets, mobile devices, or sensor systems. In the edge computation systems of IoT in the smart city, the fundamental difficulty of the sensor is to pick reliable participants. Since not all smart IoT gadgets are dedicated, certain intelligent IoT gadgets might destroy the networks or services deliberately and degrade the customer experience. A trust-based internet of things (TM-IoT) cloud computing method is proposed in this research. The problem is solved by choosing trustworthy partners to enhance the quality services of the IoT edging network in the Smart architectures. A smart device choice recommendation method based on the changing networks was developed. It applied the evolutionary concept of games to examine the reliability and durability of the technique of trust management presented in this article. The reliability and durability of the trustworthiness-managing system, the Lyapunov concept was applied.A real scenario for personal-health-control systems and air-qualitymonitoring and assessment in a smart city setting confirmed the efficiency of the confidence-management mechanism. Experiments have demonstrated that the methodology for trust administration suggested in this research plays a major part in promoting multi-intelligent gadget collaboration in the IoT edge computer system with an efficiency of 97%. It resists harmful threads against service suppliers more consistently and is ideal for the smart world's massive IoT edge computer system.
引用
收藏
页码:1765 / 1777
页数:12
相关论文
共 123 条
[1]  
Nguyen NT(2016)On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees Comput. Netw. 105 99-110
[2]  
Liu BH(2020)Creating collision-free communication in IoT with 6G using multiple machine access learning collision avoidance protocol Mobile Netw. Appl. 26 1-12
[3]  
Pham VT(2020)Task failure prediction in cloud data centers using deep learning IEEE Trans. Serv. Comput. 26 1193-1203
[4]  
Luo YS(2018)Modeling of cloud-based digital twins for smart manufacturing with MT connect Procedia Manuf. 49 101522-13
[5]  
Shakeel PM(2019)An efficient anonymous mutual authentication technique for providing secure communication in mobile cloud computing for smart city applications Sustain. Cities Soc. 14 1-3554
[6]  
Baskar S(2021)AI assisted service virtualization and flow management framework for 6G-enabled cloud-software-defined network based IoT IEEE Internet Things J 75 3534-1394
[7]  
Fouad H(2020)FAST: fast accessing scheme for data transmission in cloud computing Peer-to-Peer Netw. Appl. 86 1383-507
[8]  
Manogaran G(2019)A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments J. Supercomput. 8 3360-138
[9]  
Saravanan V(2018)A hybrid model of Internet of things and cloud computing to manage big data in health services applications Future Gener. Comput. Syst. 39 499-98
[10]  
Xin Q(2020)A response-aware traffic offloading scheme using regression machine learning for user-centric large-scale internet of things IEEE Internet Things J. 347 131-43