A verifiable trust evaluation mechanism for ultra-reliable applications in 5G and beyond networks

Abstract With the development of Internet of Thing (IoT) joint 5G and Beyond Networks, Mobile Edge Users (MEUs) can act as mobile data collectors to collect data for various applications. However, some malicious MEUs reporting false or malicious data can cause serious harm to applications, especially for ultra-reliable applications. A novel Verifiable Trust Evaluation joint UAV (VTE-UAV) mechanism is proposed to select trustworthy MEUs to conduct the task for ultra-reliable applications. The VTE-UAV strategy adopts two novel trust evaluation methods, one is the aggregation-based MEU trust evaluation mechanism, when malicious evaluation objects are in the minority, the mechanism takes most evaluation results as baseline data. The other is an active trust acquisition mechanism, it takes the data obtained by Unmanned Aerial Vehicles (UAVs) as baseline data to actively validate the authenticity of the data. Through these two cross evaluation strategies, we obtain more accurate trust evaluation results. Finally, this paper transforms the trust evaluation optimization problem into the optimization of the accuracy of trust evaluation with reducing the cloud payment and the dispatch cost of UAVs. Extensive experiments have verified the validity of the VTE-UAV strategy. Compared with the previous strategies, the VTE-UAV improves the cloud recruitment performance by 7.74%-25.91%, increases the accuracy of trust evaluation of IoT devices by 2.24%-11.72%, and reduces the cloud payment and the cost of UAVs by 3.11%-10.20% and 58.23%, respectively.

[1]  Rui Zhang,et al.  Energy-Efficient UAV Communication With Trajectory Optimization , 2016, IEEE Transactions on Wireless Communications.

[2]  Zhiguo Shi,et al.  Noise-Aware DVFS for Efficient Transitions on Battery-Powered IoT Devices , 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[3]  Jianbo Xu,et al.  A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering , 2021, ACM Trans. Multim. Comput. Commun. Appl..

[4]  Dong Zheng,et al.  Security and Privacy Challenges in 5G-Enabled Vehicular Networks , 2020, IEEE Network.

[5]  LiangWei,et al.  A Fast Defogging Image Recognition Algorithm based on Bilateral Hybrid Filtering , 2020 .

[6]  Sudip Misra,et al.  Assessment of the Suitability of Fog Computing in the Context of Internet of Things , 2018, IEEE Transactions on Cloud Computing.

[7]  Qichao Xu,et al.  Blockchain-Based Trustworthy Edge Caching Scheme for Mobile Cyber-Physical System , 2020, IEEE Internet of Things Journal.

[8]  Bo Jiang,et al.  Trust based energy efficient data collection with unmanned aerial vehicle in edge network , 2020, Trans. Emerg. Telecommun. Technol..

[9]  Anfeng Liu,et al.  An Intelligent Edge-Computing-Based Method to Counter Coupling Problems in Cyber-Physical Systems , 2020, IEEE Network.

[10]  Kaoru Ota,et al.  Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems , 2020, Peer-to-Peer Netw. Appl..

[11]  Jon W. Mark,et al.  Performance Analysis and Enhancement of the DSRC for VANET's Safety Applications , 2013, IEEE Trans. Veh. Technol..

[12]  Xiaoheng Deng,et al.  QoE-driven computation offloading for Edge Computing , 2019, J. Syst. Archit..

[13]  Xi Zheng,et al.  Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing , 2019, ACM Trans. Intell. Syst. Technol..

[14]  Anfeng Liu,et al.  Intelligent UAVs Trajectory Optimization From Space-Time for Data Collection in Social Networks , 2021, IEEE Transactions on Network Science and Engineering.

[15]  Dafang Zhang,et al.  Circuit Copyright Blockchain: Blockchain-Based Homomorphic Encryption for IP Circuit Protection , 2021, IEEE Transactions on Emerging Topics in Computing.

[16]  Dafang Zhang,et al.  Secure Data Storage and Recovery in Industrial Blockchain Network Environments , 2020, IEEE Transactions on Industrial Informatics.

[17]  Jun Li,et al.  Task allocation algorithm and optimization model on edge collaboration , 2020, J. Syst. Archit..

[18]  Tao Peng,et al.  Intelligent route planning on large road networks with efficiency and privacy , 2019, J. Parallel Distributed Comput..

[19]  Shichao Zhang,et al.  PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification , 2019, Neurocomputing.

[20]  Jinsong Gui,et al.  Joint mobile vehicle–UAV scheme for secure data collection in a smart city , 2020, Annals of Telecommunications.

[21]  Hao Liang,et al.  Dynamic Spectrum Access in Multi-Channel Cognitive Radio Networks , 2014, IEEE Journal on Selected Areas in Communications.

[22]  Tian Wang,et al.  Artificial intelligence aware and security-enhanced traceback technique in mobile edge computing , 2020, Comput. Commun..

[23]  Guojun Wang,et al.  Enabling Verifiable and Dynamic Ranked Search over Outsourced Data , 2019, IEEE Transactions on Services Computing.

[24]  Zhiwen Zeng,et al.  A trust-based minimum cost and quality aware data collection scheme in P2P network , 2020, Peer-to-Peer Netw. Appl..

[25]  Jinhuan Zhang,et al.  An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network , 2020, Personal and Ubiquitous Computing.

[26]  Wei Liu,et al.  Trust data collections via vehicles joint with unmanned aerial vehicles in the smart Internet of Things , 2020, Trans. Emerg. Telecommun. Technol..

[27]  Yong Zhou,et al.  Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface , 2020, IEEE Network.

[28]  Arun Kumar Sangaiah,et al.  Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things , 2020, IEEE Internet of Things Journal.

[29]  Shigeng Zhang,et al.  Time-Efficient Target Tags Information Collection in Large-Scale RFID Systems , 2020, IEEE Transactions on Mobile Computing.

[30]  Xiaolong Li,et al.  Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles , 2020, IEEE Transactions on Industrial Informatics.

[31]  Bin Liu,et al.  Q‐learning based flexible task scheduling in a global view for the Internet of Things , 2020, Trans. Emerg. Telecommun. Technol..

[32]  Jinsong Gui,et al.  Stabilizing Transmission Capacity in Millimeter Wave Links by Q-Learning-Based Scheme , 2020, Mob. Inf. Syst..

[33]  Jiannong Cao,et al.  Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution , 2017, IEEE Transactions on Mobile Computing.

[34]  Tian Wang,et al.  Energy-aware MAC protocol for data differentiated services in sensor-cloud computing , 2020, J. Cloud Comput..

[35]  Arun Kumar Sangaiah,et al.  Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities , 2021, IEEE Transactions on Intelligent Transportation Systems.

[36]  Zhiwen Zeng,et al.  An AUV-Assisted Data Gathering Scheme Based on Clustering and Matrix Completion for Smart Ocean , 2020, IEEE Internet of Things Journal.

[37]  Keqin Li,et al.  A double PUF-based RFID identity authentication protocol in service-centric internet of things environments , 2019, Inf. Sci..

[38]  Jiangtao Wang,et al.  HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing , 2018, IEEE Transactions on Mobile Computing.

[39]  Anfeng Liu,et al.  A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems , 2021, Inf. Sci..

[40]  Anfeng Liu,et al.  Objective-Variable Tour Planning for Mobile Data Collection in Partitioned Sensor Networks , 2022, IEEE Transactions on Mobile Computing.

[41]  Ming Zhao,et al.  A high‐accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology , 2020, Trans. Emerg. Telecommun. Technol..

[42]  Tie Qiu,et al.  Restoring Connectivity of Damaged Sensor Networks for Long-Term Survival in Hostile Environments , 2020, IEEE Internet of Things Journal.

[43]  Yongmin Zhang,et al.  Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks , 2020, IEEE/ACM Transactions on Networking.

[44]  G. Bianchi,et al.  Opportunistic communication in smart city: Experimental insight with small-scale taxi fleets as data carriers , 2016, Ad Hoc Networks.

[45]  Rongxing Lu,et al.  Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top- $k$ Spatial–Textual Query , 2019, IEEE Internet of Things Journal.

[46]  Mianxiong Dong,et al.  ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks , 2016, IEEE Transactions on Information Forensics and Security.

[47]  Zhiwen Zeng,et al.  A Novel Load Balancing and Low Response Delay Framework for Edge-Cloud Network Based on SDN , 2020, IEEE Internet of Things Journal.

[48]  Anfeng Liu,et al.  BD-VTE: A Novel Baseline Data Based Verifiable Trust Evaluation Scheme for Smart Network Systems , 2021, IEEE Transactions on Network Science and Engineering.

[49]  Xuemin Shen,et al.  Cloud assisted HetNets toward 5G wireless networks , 2015, IEEE Communications Magazine.

[50]  Jinhuan Zhang,et al.  An active and verifiable trust evaluation approach for edge computing , 2020, Journal of Cloud Computing.